Time_Series_Assignment_Variable_Y1_Regression_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series and Forecasting\tAssignment\n",
    "\n",
    "### Course:\tTime Series\tand\tforecasting\tmethods\n",
    "#### Program: MSc in Data Science (part-time)\n",
    "\n",
    "\n",
    "* Student: **Perakis Georgios**\n",
    "* Code: **DS3616014**\n",
    "* Contact: **gperakis[at]aueb.gr**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data you will have to analyze are in the eclass in the **excel data-assignment.xls**\n",
    "file. The **dependent variables** for which you will construct the models you are asked\n",
    "for, are the **returns** of four investment vechicles **(Y1, Y2, Y3, Y4)** for the period **1/1991 - 12/2005**.\n",
    "\n",
    "The **independent variables** you will use in the models refer to **monthly** values/returns for the variables:\n",
    "* x1 = RUS-Rf\n",
    "* x2 = RUS (-1) -Rf (-1) lagged Russel index\n",
    "* x3 = MXUS-Rf\n",
    "* x4 = MEM- Rf\n",
    "* x5 = SMB\n",
    "* x6 = HML\n",
    "* x7 = MOM\n",
    "* x8 = SBGC-Rf\n",
    "* x9 = SBWG-Rf\n",
    "* x10 = LHY-Rf\n",
    "* x11 = DEFSPR\n",
    "* x12 = FRSI-Rf\n",
    "* x13 = GSCI-Rf\n",
    "* x14 = VIX\n",
    "\n",
    "for the period **1/1991 - 12/2005**.\n",
    "\n",
    "Analyze dependent variables based on data for the period 1/1991 - 12/2004 **[You will not use the data for the period 1/2005 - 12/2005]**:\n",
    "\n",
    "1. Construct an appropriate **time series** model (AR, MA, ARMA).\n",
    "2. Develop an appropriate **regression** model\n",
    "  * In case of **autocorrelation** problem of **regression residuals**, correct the autocorrelation problem (using time series AR, MA, ARMA models).\n",
    "  * In case of **heteroscedasticity** problem of **regression residuals**, correct the\n",
    "    heteroskedasticity problem (using time-varying ARCH, GARCH models).\n",
    "3. Write the models you have found at questions (1) - (2). Assess the **goodness of fit** of these models based on the **AIC and BIC** information criteria.\n",
    "4. Based on the estimated models of questions (1) - (2), **construct forecasts** of the analyzed series for the period **1/2005 - 12/2005**, and **evaluate the forecasts** you have found by using two evaluation criteria:\n",
    "  * the mean square prediction error\n",
    "  * the Hit ratio (indicates the percentage of predictions that correctly evaluate the sign of the actual value of the dependent variable.)\n",
    "  \n",
    "Each student will have to analyze **two only dependent variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2:\n",
    "\n",
    "### Develop an appropriate regression model\n",
    "\n",
    "\n",
    "The constructed model will be of the form:\n",
    "\n",
    "$$y_{t+1} = a + b_{1}* x_{1, t} +  ... +  b_{k}* x_{k, t} + e_{t + 1}$$\n",
    "\n",
    "with $$e_{t + 1} \\thicksim N(0, σ^2)$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: timeDate\n",
      "Loading required package: timeSeries\n",
      "Loading required package: fBasics\n"
     ]
    }
   ],
   "source": [
    "# loads the library urca, which contains the command ur.df() \n",
    "# and applies Unit-Root Testing based on Dickey-Fuller test \n",
    "# install.packages(c('fGarch', 'urca','tseries','MASS'))\n",
    "library(urca)\n",
    "library(tseries)\n",
    "library(MASS)\n",
    "library(fGarch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data into R can be carried out in various ways. Below, the command read.table is used:\n",
    "data_all = read.table('/home/george/AUEB/TimeSeries/time_series_in_r/Data-Assignment-Feb-2018.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata_cols <- c('V5','V6', 'V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18')\n",
    "\n",
    "xdata <- data_all[c(xdata_cols)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>V12</th><th scope=col>V13</th><th scope=col>V14</th><th scope=col>V15</th><th scope=col>V16</th><th scope=col>V17</th><th scope=col>V18</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0.04568</td><td> 0.01745</td><td> 0.02313</td><td> 0.07273</td><td> 0.0389 </td><td>-0.0181 </td><td>-0.0946 </td><td> 0.00575</td><td> 0.01937</td><td> 0.01868</td><td> 0.0001 </td><td> 0.00004</td><td>-0.10389</td><td>-0.0547 </td></tr>\n",
       "\t<tr><td> 0.06346</td><td> 0.04568</td><td> 0.09344</td><td> 0.13201</td><td> 0.0397 </td><td>-0.0056 </td><td>-0.1147 </td><td> 0.00144</td><td>-0.00486</td><td> 0.10639</td><td>-0.0014 </td><td>-0.01039</td><td> 0.00866</td><td> 0.0032 </td></tr>\n",
       "\t<tr><td> 0.02225</td><td> 0.06346</td><td>-0.06591</td><td> 0.03187</td><td> 0.0395 </td><td>-0.0124 </td><td>-0.0407 </td><td> 0.00253</td><td>-0.04193</td><td> 0.02621</td><td>-0.0024 </td><td> 0.03228</td><td> 0.03201</td><td>-0.0435 </td></tr>\n",
       "\t<tr><td>-0.00581</td><td> 0.02225</td><td> 0.00371</td><td>-0.00174</td><td> 0.0037 </td><td> 0.0170 </td><td>-0.0224 </td><td> 0.00790</td><td> 0.01053</td><td> 0.01619</td><td>-0.0008 </td><td> 0.01107</td><td> 0.01764</td><td> 0.0136 </td></tr>\n",
       "\t<tr><td> 0.03258</td><td>-0.00581</td><td> 0.00577</td><td> 0.06795</td><td>-0.0037 </td><td>-0.0052 </td><td> 0.0282 </td><td>-0.00023</td><td>-0.00601</td><td>-0.00780</td><td>-0.0011 </td><td> 0.00235</td><td>-0.00524</td><td>-0.0231 </td></tr>\n",
       "\t<tr><td>-0.05360</td><td> 0.03258</td><td>-0.07982</td><td>-0.04322</td><td> 0.0006 </td><td> 0.0102 </td><td> 0.0010 </td><td>-0.00499</td><td>-0.01526</td><td> 0.01826</td><td>-0.0011 </td><td> 0.01085</td><td>-0.02869</td><td> 0.0362 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14 & V15 & V16 & V17 & V18\\\\\n",
       "\\hline\n",
       "\t  0.04568 &  0.01745 &  0.02313 &  0.07273 &  0.0389  & -0.0181  & -0.0946  &  0.00575 &  0.01937 &  0.01868 &  0.0001  &  0.00004 & -0.10389 & -0.0547 \\\\\n",
       "\t  0.06346 &  0.04568 &  0.09344 &  0.13201 &  0.0397  & -0.0056  & -0.1147  &  0.00144 & -0.00486 &  0.10639 & -0.0014  & -0.01039 &  0.00866 &  0.0032 \\\\\n",
       "\t  0.02225 &  0.06346 & -0.06591 &  0.03187 &  0.0395  & -0.0124  & -0.0407  &  0.00253 & -0.04193 &  0.02621 & -0.0024  &  0.03228 &  0.03201 & -0.0435 \\\\\n",
       "\t -0.00581 &  0.02225 &  0.00371 & -0.00174 &  0.0037  &  0.0170  & -0.0224  &  0.00790 &  0.01053 &  0.01619 & -0.0008  &  0.01107 &  0.01764 &  0.0136 \\\\\n",
       "\t  0.03258 & -0.00581 &  0.00577 &  0.06795 & -0.0037  & -0.0052  &  0.0282  & -0.00023 & -0.00601 & -0.00780 & -0.0011  &  0.00235 & -0.00524 & -0.0231 \\\\\n",
       "\t -0.05360 &  0.03258 & -0.07982 & -0.04322 &  0.0006  &  0.0102  &  0.0010  & -0.00499 & -0.01526 &  0.01826 & -0.0011  &  0.01085 & -0.02869 &  0.0362 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | \n",
       "|---|---|---|---|---|---|\n",
       "|  0.04568 |  0.01745 |  0.02313 |  0.07273 |  0.0389  | -0.0181  | -0.0946  |  0.00575 |  0.01937 |  0.01868 |  0.0001  |  0.00004 | -0.10389 | -0.0547  | \n",
       "|  0.06346 |  0.04568 |  0.09344 |  0.13201 |  0.0397  | -0.0056  | -0.1147  |  0.00144 | -0.00486 |  0.10639 | -0.0014  | -0.01039 |  0.00866 |  0.0032  | \n",
       "|  0.02225 |  0.06346 | -0.06591 |  0.03187 |  0.0395  | -0.0124  | -0.0407  |  0.00253 | -0.04193 |  0.02621 | -0.0024  |  0.03228 |  0.03201 | -0.0435  | \n",
       "| -0.00581 |  0.02225 |  0.00371 | -0.00174 |  0.0037  |  0.0170  | -0.0224  |  0.00790 |  0.01053 |  0.01619 | -0.0008  |  0.01107 |  0.01764 |  0.0136  | \n",
       "|  0.03258 | -0.00581 |  0.00577 |  0.06795 | -0.0037  | -0.0052  |  0.0282  | -0.00023 | -0.00601 | -0.00780 | -0.0011  |  0.00235 | -0.00524 | -0.0231  | \n",
       "| -0.05360 |  0.03258 | -0.07982 | -0.04322 |  0.0006  |  0.0102  |  0.0010  | -0.00499 | -0.01526 |  0.01826 | -0.0011  |  0.01085 | -0.02869 |  0.0362  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  V5       V6       V7       V8       V9      V10     V11     V12      V13     \n",
       "1  0.04568  0.01745  0.02313  0.07273  0.0389 -0.0181 -0.0946  0.00575  0.01937\n",
       "2  0.06346  0.04568  0.09344  0.13201  0.0397 -0.0056 -0.1147  0.00144 -0.00486\n",
       "3  0.02225  0.06346 -0.06591  0.03187  0.0395 -0.0124 -0.0407  0.00253 -0.04193\n",
       "4 -0.00581  0.02225  0.00371 -0.00174  0.0037  0.0170 -0.0224  0.00790  0.01053\n",
       "5  0.03258 -0.00581  0.00577  0.06795 -0.0037 -0.0052  0.0282 -0.00023 -0.00601\n",
       "6 -0.05360  0.03258 -0.07982 -0.04322  0.0006  0.0102  0.0010 -0.00499 -0.01526\n",
       "  V14      V15     V16      V17      V18    \n",
       "1  0.01868  0.0001  0.00004 -0.10389 -0.0547\n",
       "2  0.10639 -0.0014 -0.01039  0.00866  0.0032\n",
       "3  0.02621 -0.0024  0.03228  0.03201 -0.0435\n",
       "4  0.01619 -0.0008  0.01107  0.01764  0.0136\n",
       "5 -0.00780 -0.0011  0.00235 -0.00524 -0.0231\n",
       "6  0.01826 -0.0011  0.01085 -0.02869  0.0362"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(xdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Let create a time series object using the function “ts” from a vector - single time-series or a matrix - multivariate time-series. The data consist of the Y1 monthly returns from 1/1991 - 12/2004, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 <- data_all$V1\n",
    "\n",
    "j1 = ts(y1, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "xfactors=ts(xdata, frequency=12,  start = c(1991, 1), end = c(2004, 12))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data_all[1: 168,]\n",
    "test_data = data_all[169:180,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "attach(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing Stepwise Linear Regression will all the parameters, in order to find the best model with the lower AIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=-1315.73\n",
      "V1 ~ 1\n",
      "\n",
      "       Df Sum of Sq      RSS     AIC\n",
      "+ V8    1  0.041858 0.024039 -1483.1\n",
      "+ V5    1  0.037818 0.028079 -1457.0\n",
      "+ V7    1  0.026177 0.039720 -1398.8\n",
      "+ V9    1  0.020163 0.045734 -1375.1\n",
      "+ V14   1  0.017107 0.048790 -1364.2\n",
      "+ V18   1  0.016947 0.048950 -1363.7\n",
      "+ V10   1  0.011213 0.054684 -1345.1\n",
      "+ V15   1  0.004390 0.061507 -1325.3\n",
      "+ V6    1  0.002189 0.063708 -1319.4\n",
      "+ V11   1  0.001875 0.064023 -1318.6\n",
      "+ V17   1  0.001405 0.064492 -1317.3\n",
      "+ V16   1  0.001129 0.064768 -1316.6\n",
      "<none>              0.065897 -1315.7\n",
      "+ V12   1  0.000101 0.065797 -1314.0\n",
      "+ V13   1  0.000062 0.065835 -1313.9\n",
      "\n",
      "Step:  AIC=-1483.14\n",
      "V1 ~ V8\n",
      "\n",
      "       Df Sum of Sq      RSS     AIC\n",
      "+ V9    1  0.005287 0.018752 -1522.9\n",
      "+ V5    1  0.005032 0.019007 -1520.6\n",
      "+ V10   1  0.005032 0.019008 -1520.6\n",
      "+ V7    1  0.001612 0.022427 -1492.8\n",
      "+ V14   1  0.001245 0.022794 -1490.1\n",
      "+ V6    1  0.000756 0.023283 -1486.5\n",
      "+ V12   1  0.000663 0.023376 -1485.8\n",
      "+ V17   1  0.000546 0.023493 -1485.0\n",
      "+ V11   1  0.000368 0.023671 -1483.7\n",
      "<none>              0.024039 -1483.1\n",
      "+ V18   1  0.000233 0.023806 -1482.8\n",
      "+ V15   1  0.000182 0.023857 -1482.4\n",
      "+ V16   1  0.000153 0.023886 -1482.2\n",
      "+ V13   1  0.000002 0.024037 -1481.2\n",
      "- V8    1  0.041858 0.065897 -1315.7\n",
      "\n",
      "Step:  AIC=-1522.87\n",
      "V1 ~ V8 + V9\n",
      "\n",
      "       Df Sum of Sq      RSS     AIC\n",
      "+ V5    1 0.0071071 0.011645 -1600.9\n",
      "+ V10   1 0.0025342 0.016218 -1545.3\n",
      "+ V7    1 0.0019857 0.016767 -1539.7\n",
      "+ V12   1 0.0012918 0.017460 -1532.9\n",
      "+ V11   1 0.0006566 0.018096 -1526.9\n",
      "+ V18   1 0.0003916 0.018361 -1524.4\n",
      "+ V14   1 0.0003076 0.018445 -1523.7\n",
      "+ V17   1 0.0002649 0.018487 -1523.3\n",
      "<none>              0.018752 -1522.9\n",
      "+ V16   1 0.0001532 0.018599 -1522.2\n",
      "+ V6    1 0.0001383 0.018614 -1522.1\n",
      "+ V13   1 0.0000421 0.018710 -1521.2\n",
      "+ V15   1 0.0000165 0.018736 -1521.0\n",
      "- V9    1 0.0052869 0.024039 -1483.1\n",
      "- V8    1 0.0269820 0.045734 -1375.1\n",
      "\n",
      "Step:  AIC=-1600.91\n",
      "V1 ~ V8 + V9 + V5\n",
      "\n",
      "       Df Sum of Sq      RSS     AIC\n",
      "+ V11   1 0.0011216 0.010523 -1615.9\n",
      "+ V10   1 0.0009423 0.010703 -1613.1\n",
      "+ V12   1 0.0007090 0.010936 -1609.5\n",
      "+ V18   1 0.0006040 0.011041 -1607.9\n",
      "+ V17   1 0.0003433 0.011302 -1603.9\n",
      "+ V6    1 0.0003249 0.011320 -1603.7\n",
      "<none>              0.011645 -1600.9\n",
      "+ V16   1 0.0001376 0.011507 -1600.9\n",
      "+ V7    1 0.0000122 0.011633 -1599.1\n",
      "+ V14   1 0.0000050 0.011640 -1599.0\n",
      "+ V13   1 0.0000049 0.011640 -1599.0\n",
      "+ V15   1 0.0000011 0.011644 -1598.9\n",
      "- V8    1 0.0032002 0.014845 -1562.1\n",
      "- V5    1 0.0071071 0.018752 -1522.9\n",
      "- V9    1 0.0073619 0.019007 -1520.6\n",
      "\n",
      "Step:  AIC=-1615.92\n",
      "V1 ~ V8 + V9 + V5 + V11\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V6    1 0.0007130 0.0098105 -1625.7\n",
      "+ V18   1 0.0006064 0.0099170 -1623.9\n",
      "+ V12   1 0.0005031 0.0100204 -1622.2\n",
      "+ V17   1 0.0002116 0.0103118 -1617.3\n",
      "+ V14   1 0.0001547 0.0103688 -1616.4\n",
      "+ V10   1 0.0001475 0.0103760 -1616.3\n",
      "+ V16   1 0.0001434 0.0103800 -1616.2\n",
      "<none>              0.0105235 -1615.9\n",
      "+ V15   1 0.0000351 0.0104883 -1614.5\n",
      "+ V13   1 0.0000078 0.0105157 -1614.0\n",
      "+ V7    1 0.0000000 0.0105234 -1613.9\n",
      "- V11   1 0.0011216 0.0116451 -1600.9\n",
      "- V8    1 0.0036254 0.0141489 -1568.2\n",
      "- V5    1 0.0075722 0.0180956 -1526.9\n",
      "- V9    1 0.0078783 0.0184017 -1524.0\n",
      "\n",
      "Step:  AIC=-1625.71\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V12   1 0.0005770 0.0092334 -1633.9\n",
      "+ V18   1 0.0003710 0.0094395 -1630.2\n",
      "+ V17   1 0.0003167 0.0094938 -1629.2\n",
      "+ V10   1 0.0002762 0.0095343 -1628.5\n",
      "+ V16   1 0.0001676 0.0096429 -1626.6\n",
      "<none>              0.0098105 -1625.7\n",
      "+ V14   1 0.0000812 0.0097293 -1625.1\n",
      "+ V7    1 0.0000024 0.0098081 -1623.8\n",
      "+ V15   1 0.0000014 0.0098091 -1623.7\n",
      "+ V13   1 0.0000000 0.0098105 -1623.7\n",
      "- V6    1 0.0007130 0.0105235 -1615.9\n",
      "- V11   1 0.0015097 0.0113202 -1603.7\n",
      "- V8    1 0.0035248 0.0133353 -1576.1\n",
      "- V9    1 0.0067506 0.0165611 -1539.7\n",
      "- V5    1 0.0079583 0.0177688 -1527.9\n",
      "\n",
      "Step:  AIC=-1633.89\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V10   1 0.0004011 0.0088324 -1639.3\n",
      "+ V13   1 0.0002800 0.0089535 -1637.1\n",
      "+ V16   1 0.0002757 0.0089578 -1637.0\n",
      "+ V18   1 0.0002662 0.0089672 -1636.8\n",
      "+ V17   1 0.0002618 0.0089717 -1636.7\n",
      "<none>              0.0092334 -1633.9\n",
      "+ V15   1 0.0000244 0.0092090 -1632.3\n",
      "+ V7    1 0.0000043 0.0092291 -1632.0\n",
      "+ V14   1 0.0000028 0.0092306 -1631.9\n",
      "- V12   1 0.0005770 0.0098105 -1625.7\n",
      "- V6    1 0.0007869 0.0100204 -1622.2\n",
      "- V11   1 0.0012857 0.0105192 -1614.0\n",
      "- V8    1 0.0037291 0.0129626 -1578.9\n",
      "- V9    1 0.0070568 0.0162903 -1540.5\n",
      "- V5    1 0.0073479 0.0165813 -1537.5\n",
      "\n",
      "Step:  AIC=-1639.35\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V16   1 0.0003276 0.0085047 -1643.7\n",
      "+ V13   1 0.0002811 0.0085513 -1642.8\n",
      "+ V17   1 0.0002617 0.0085707 -1642.4\n",
      "+ V18   1 0.0001701 0.0086623 -1640.6\n",
      "<none>              0.0088324 -1639.3\n",
      "+ V15   1 0.0000246 0.0088077 -1637.8\n",
      "+ V14   1 0.0000185 0.0088139 -1637.7\n",
      "+ V7    1 0.0000031 0.0088293 -1637.4\n",
      "- V11   1 0.0002453 0.0090776 -1636.8\n",
      "- V10   1 0.0004011 0.0092334 -1633.9\n",
      "- V12   1 0.0007019 0.0095343 -1628.5\n",
      "- V6    1 0.0009624 0.0097947 -1624.0\n",
      "- V9    1 0.0038169 0.0126493 -1581.0\n",
      "- V8    1 0.0039126 0.0127450 -1579.8\n",
      "- V5    1 0.0051733 0.0140057 -1563.9\n",
      "\n",
      "Step:  AIC=-1643.7\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10 + V16\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V17   1 0.0002720 0.0082327 -1647.2\n",
      "+ V18   1 0.0002215 0.0082833 -1646.1\n",
      "<none>              0.0085047 -1643.7\n",
      "+ V13   1 0.0000662 0.0084385 -1643.0\n",
      "+ V14   1 0.0000127 0.0084920 -1642.0\n",
      "+ V7    1 0.0000074 0.0084973 -1641.8\n",
      "+ V15   1 0.0000002 0.0085046 -1641.7\n",
      "- V11   1 0.0002195 0.0087242 -1641.4\n",
      "- V16   1 0.0003276 0.0088324 -1639.3\n",
      "- V10   1 0.0004530 0.0089578 -1637.0\n",
      "- V12   1 0.0008386 0.0093433 -1629.9\n",
      "- V6    1 0.0010237 0.0095284 -1626.6\n",
      "- V9    1 0.0037416 0.0122463 -1584.5\n",
      "- V8    1 0.0041894 0.0126941 -1578.4\n",
      "- V5    1 0.0050417 0.0135465 -1567.5\n",
      "\n",
      "Step:  AIC=-1647.16\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10 + V16 + V17\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V18   1 0.0002160 0.0080168 -1649.6\n",
      "<none>              0.0082327 -1647.2\n",
      "+ V13   1 0.0000851 0.0081477 -1646.9\n",
      "+ V14   1 0.0000164 0.0082163 -1645.5\n",
      "- V11   1 0.0001873 0.0084200 -1645.4\n",
      "+ V15   1 0.0000025 0.0082302 -1645.2\n",
      "+ V7    1 0.0000004 0.0082323 -1645.2\n",
      "- V17   1 0.0002720 0.0085047 -1643.7\n",
      "- V16   1 0.0003380 0.0085707 -1642.4\n",
      "- V10   1 0.0004538 0.0086865 -1640.2\n",
      "- V12   1 0.0007744 0.0090071 -1634.1\n",
      "- V6    1 0.0011320 0.0093648 -1627.5\n",
      "- V9    1 0.0034682 0.0117009 -1590.1\n",
      "- V8    1 0.0040492 0.0122819 -1582.0\n",
      "- V5    1 0.0051112 0.0133439 -1568.0\n",
      "\n",
      "Step:  AIC=-1649.63\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10 + V16 + V17 + V18\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "+ V13   1 0.0001265 0.0078902 -1650.3\n",
      "<none>              0.0080168 -1649.6\n",
      "+ V14   1 0.0000216 0.0079952 -1648.1\n",
      "+ V7    1 0.0000016 0.0080151 -1647.7\n",
      "+ V15   1 0.0000002 0.0080165 -1647.6\n",
      "- V18   1 0.0002160 0.0082327 -1647.2\n",
      "- V11   1 0.0002180 0.0082348 -1647.1\n",
      "- V17   1 0.0002665 0.0082833 -1646.1\n",
      "- V10   1 0.0003441 0.0083608 -1644.6\n",
      "- V16   1 0.0003893 0.0084060 -1643.7\n",
      "- V12   1 0.0006545 0.0086713 -1638.5\n",
      "- V6    1 0.0008363 0.0088531 -1635.0\n",
      "- V9    1 0.0036404 0.0116571 -1588.7\n",
      "- V8    1 0.0042435 0.0122603 -1580.3\n",
      "- V5    1 0.0047580 0.0127748 -1573.3\n",
      "\n",
      "Step:  AIC=-1650.3\n",
      "V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10 + V16 + V17 + V18 + \n",
      "    V13\n",
      "\n",
      "       Df Sum of Sq       RSS     AIC\n",
      "<none>              0.0078902 -1650.3\n",
      "- V16   1 0.0001084 0.0079986 -1650.0\n",
      "- V13   1 0.0001265 0.0080168 -1649.6\n",
      "+ V7    1 0.0000487 0.0078415 -1649.3\n",
      "+ V14   1 0.0000174 0.0078728 -1648.7\n",
      "+ V15   1 0.0000033 0.0078869 -1648.4\n",
      "- V11   1 0.0002498 0.0081400 -1647.1\n",
      "- V18   1 0.0002575 0.0081477 -1646.9\n",
      "- V17   1 0.0002893 0.0081795 -1646.3\n",
      "- V10   1 0.0003149 0.0082052 -1645.7\n",
      "- V6    1 0.0007151 0.0086053 -1637.7\n",
      "- V12   1 0.0007525 0.0086427 -1637.0\n",
      "- V9    1 0.0037142 0.0116044 -1587.5\n",
      "- V8    1 0.0041192 0.0120094 -1581.7\n",
      "- V5    1 0.0048514 0.0127416 -1571.8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Step</th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th><th scope=col>AIC</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>            </td><td>NA          </td><td>          NA</td><td>167         </td><td>0.065897077 </td><td>-1315.729   </td></tr>\n",
       "\t<tr><td>+ V8        </td><td>-1          </td><td>0.0418579518</td><td>166         </td><td>0.024039125 </td><td>-1483.142   </td></tr>\n",
       "\t<tr><td>+ V9        </td><td>-1          </td><td>0.0052868954</td><td>165         </td><td>0.018752229 </td><td>-1522.868   </td></tr>\n",
       "\t<tr><td>+ V5        </td><td>-1          </td><td>0.0071071361</td><td>164         </td><td>0.011645093 </td><td>-1600.908   </td></tr>\n",
       "\t<tr><td>+ V11       </td><td>-1          </td><td>0.0011216325</td><td>163         </td><td>0.010523461 </td><td>-1615.923   </td></tr>\n",
       "\t<tr><td>+ V6        </td><td>-1          </td><td>0.0007129730</td><td>162         </td><td>0.009810488 </td><td>-1625.709   </td></tr>\n",
       "\t<tr><td>+ V12       </td><td>-1          </td><td>0.0005770391</td><td>161         </td><td>0.009233449 </td><td>-1633.893   </td></tr>\n",
       "\t<tr><td>+ V10       </td><td>-1          </td><td>0.0004010867</td><td>160         </td><td>0.008832362 </td><td>-1639.354   </td></tr>\n",
       "\t<tr><td>+ V16       </td><td>-1          </td><td>0.0003276195</td><td>159         </td><td>0.008504742 </td><td>-1643.704   </td></tr>\n",
       "\t<tr><td>+ V17       </td><td>-1          </td><td>0.0002720034</td><td>158         </td><td>0.008232739 </td><td>-1647.165   </td></tr>\n",
       "\t<tr><td>+ V18       </td><td>-1          </td><td>0.0002159717</td><td>157         </td><td>0.008016767 </td><td>-1649.631   </td></tr>\n",
       "\t<tr><td>+ V13       </td><td>-1          </td><td>0.0001265497</td><td>156         </td><td>0.007890218 </td><td>-1650.304   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " Step & Df & Deviance & Resid. Df & Resid. Dev & AIC\\\\\n",
       "\\hline\n",
       "\t              & NA           &           NA & 167          & 0.065897077  & -1315.729   \\\\\n",
       "\t + V8         & -1           & 0.0418579518 & 166          & 0.024039125  & -1483.142   \\\\\n",
       "\t + V9         & -1           & 0.0052868954 & 165          & 0.018752229  & -1522.868   \\\\\n",
       "\t + V5         & -1           & 0.0071071361 & 164          & 0.011645093  & -1600.908   \\\\\n",
       "\t + V11        & -1           & 0.0011216325 & 163          & 0.010523461  & -1615.923   \\\\\n",
       "\t + V6         & -1           & 0.0007129730 & 162          & 0.009810488  & -1625.709   \\\\\n",
       "\t + V12        & -1           & 0.0005770391 & 161          & 0.009233449  & -1633.893   \\\\\n",
       "\t + V10        & -1           & 0.0004010867 & 160          & 0.008832362  & -1639.354   \\\\\n",
       "\t + V16        & -1           & 0.0003276195 & 159          & 0.008504742  & -1643.704   \\\\\n",
       "\t + V17        & -1           & 0.0002720034 & 158          & 0.008232739  & -1647.165   \\\\\n",
       "\t + V18        & -1           & 0.0002159717 & 157          & 0.008016767  & -1649.631   \\\\\n",
       "\t + V13        & -1           & 0.0001265497 & 156          & 0.007890218  & -1650.304   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Step | Df | Deviance | Resid. Df | Resid. Dev | AIC | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|              | NA           |           NA | 167          | 0.065897077  | -1315.729    | \n",
       "| + V8         | -1           | 0.0418579518 | 166          | 0.024039125  | -1483.142    | \n",
       "| + V9         | -1           | 0.0052868954 | 165          | 0.018752229  | -1522.868    | \n",
       "| + V5         | -1           | 0.0071071361 | 164          | 0.011645093  | -1600.908    | \n",
       "| + V11        | -1           | 0.0011216325 | 163          | 0.010523461  | -1615.923    | \n",
       "| + V6         | -1           | 0.0007129730 | 162          | 0.009810488  | -1625.709    | \n",
       "| + V12        | -1           | 0.0005770391 | 161          | 0.009233449  | -1633.893    | \n",
       "| + V10        | -1           | 0.0004010867 | 160          | 0.008832362  | -1639.354    | \n",
       "| + V16        | -1           | 0.0003276195 | 159          | 0.008504742  | -1643.704    | \n",
       "| + V17        | -1           | 0.0002720034 | 158          | 0.008232739  | -1647.165    | \n",
       "| + V18        | -1           | 0.0002159717 | 157          | 0.008016767  | -1649.631    | \n",
       "| + V13        | -1           | 0.0001265497 | 156          | 0.007890218  | -1650.304    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   Step  Df Deviance     Resid. Df Resid. Dev  AIC      \n",
       "1        NA           NA 167       0.065897077 -1315.729\n",
       "2  + V8  -1 0.0418579518 166       0.024039125 -1483.142\n",
       "3  + V9  -1 0.0052868954 165       0.018752229 -1522.868\n",
       "4  + V5  -1 0.0071071361 164       0.011645093 -1600.908\n",
       "5  + V11 -1 0.0011216325 163       0.010523461 -1615.923\n",
       "6  + V6  -1 0.0007129730 162       0.009810488 -1625.709\n",
       "7  + V12 -1 0.0005770391 161       0.009233449 -1633.893\n",
       "8  + V10 -1 0.0004010867 160       0.008832362 -1639.354\n",
       "9  + V16 -1 0.0003276195 159       0.008504742 -1643.704\n",
       "10 + V17 -1 0.0002720034 158       0.008232739 -1647.165\n",
       "11 + V18 -1 0.0002159717 157       0.008016767 -1649.631\n",
       "12 + V13 -1 0.0001265497 156       0.007890218 -1650.304"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitnull <- lm(V1 ~ 1)\n",
    "\n",
    "stepSR <- step(fitnull, \n",
    "               scope=list(lower = ~ 1,\n",
    "                          upper = ~ V5 + V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16 + V17 + V18),\n",
    "               direction=\"both\",\n",
    "               data=train_data)\n",
    "\n",
    "stepSR$anova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = V1 ~ V8 + V9 + V5 + V11 + V6 + V12 + V10 + V16 + \n",
       "    V17 + V18 + V13)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0164400 -0.0051826 -0.0002619  0.0047843  0.0201949 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.0058549  0.0006006   9.748  < 2e-16 ***\n",
       "V8           0.1174414  0.0130136   9.025 6.34e-16 ***\n",
       "V9           0.1803062  0.0210408   8.569 9.52e-15 ***\n",
       "V5           0.2366224  0.0241604   9.794  < 2e-16 ***\n",
       "V11          0.0266261  0.0119803   2.222 0.027688 *  \n",
       "V6           0.0565637  0.0150432   3.760 0.000240 ***\n",
       "V12          0.2160292  0.0560064   3.857 0.000167 ***\n",
       "V10         -0.0497117  0.0199217  -2.495 0.013624 *  \n",
       "V16          0.0864282  0.0590474   1.464 0.145286    \n",
       "V17          0.0260040  0.0108738   2.391 0.017973 *  \n",
       "V18          0.0512296  0.0227067   2.256 0.025451 *  \n",
       "V13         -0.0689775  0.0436073  -1.582 0.115723    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.007112 on 156 degrees of freedom\n",
       "Multiple R-squared:  0.8803,\tAdjusted R-squared:  0.8718 \n",
       "F-statistic: 104.3 on 11 and 156 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(stepSR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "detach(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We'll now run a diagnostic check for the residuals of the fitted regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               Jan           Feb           Mar           Apr           May\n",
       "1991 -5.412956e-03 -8.448225e-03  1.464042e-02  7.975691e-04 -5.736115e-03\n",
       "1992  7.163441e-03  7.725163e-03  3.545627e-03  5.887146e-03  9.435022e-03\n",
       "1993  7.299773e-03  6.514941e-03  1.419287e-02  1.526544e-02  8.072780e-03\n",
       "1994  8.573122e-04 -7.958607e-03  6.748320e-03 -3.181813e-03 -3.154596e-03\n",
       "1995  2.441401e-03 -3.914028e-03  2.033055e-03  3.286789e-03 -8.167824e-03\n",
       "1996  8.811583e-03 -1.355812e-03  1.942736e-03  1.325741e-02  1.143975e-02\n",
       "1997  4.832775e-03  1.618083e-03 -6.450213e-03 -1.235877e-02  5.061081e-03\n",
       "1998 -9.143614e-03  2.311900e-03  6.679642e-04  1.406088e-03  1.349341e-03\n",
       "1999  9.697599e-04 -2.896378e-03  6.435461e-03  1.380292e-02  3.605155e-03\n",
       "2000 -4.959923e-03  7.955959e-03  8.292090e-03 -2.619757e-03 -1.797485e-03\n",
       "2001  6.580682e-03 -1.130706e-03  7.013274e-03 -8.812554e-03 -9.215826e-03\n",
       "2002 -9.143783e-03 -1.015864e-02 -5.894155e-03 -6.238339e-03  1.033527e-02\n",
       "2003  7.612116e-03 -1.500421e-03  1.328911e-03 -2.557176e-03  7.541668e-05\n",
       "2004 -2.955456e-03 -4.403093e-03 -5.274070e-03  2.833908e-03 -9.273549e-03\n",
       "               Jun           Jul           Aug           Sep           Oct\n",
       "1991  7.772458e-03  4.768078e-03 -3.835264e-03  1.688824e-02 -7.203636e-04\n",
       "1992  1.313798e-02  2.099092e-03 -6.222783e-04  5.747520e-03 -7.673789e-04\n",
       "1993  1.076841e-02  7.692190e-03  6.349902e-04 -5.152103e-03  8.869740e-03\n",
       "1994  6.189968e-03 -3.668111e-03 -1.100103e-02  1.091777e-03 -3.187250e-03\n",
       "1995  2.235850e-03  4.626790e-03  4.857560e-03  3.527510e-03 -6.594415e-03\n",
       "1996 -3.641808e-04  1.673331e-03  5.075051e-03 -6.911589e-03  1.750767e-03\n",
       "1997 -5.528122e-03  3.435636e-03  6.160176e-03  8.668585e-03 -3.040435e-03\n",
       "1998 -1.596865e-04 -1.041888e-02 -1.081629e-02 -1.644000e-02 -9.198582e-03\n",
       "1999 -3.197148e-03  1.664062e-03 -2.768300e-03 -4.531867e-03  4.559597e-03\n",
       "2000 -7.331387e-03  3.143541e-03  7.907948e-03 -3.882286e-03 -5.127158e-03\n",
       "2001 -8.712756e-03 -2.760443e-03  1.528667e-03  1.506178e-02 -1.264172e-02\n",
       "2002 -9.011079e-03  8.941282e-04 -1.907274e-03 -4.586390e-04 -5.027999e-03\n",
       "2003 -6.790316e-03 -8.358324e-03 -9.163052e-03  2.729564e-03 -3.464833e-03\n",
       "2004 -6.313338e-03 -3.798389e-04 -6.528008e-03 -7.480447e-03 -6.135583e-03\n",
       "               Nov           Dec\n",
       "1991  5.522072e-03 -3.830507e-03\n",
       "1992  3.738072e-03  2.914918e-03\n",
       "1993 -2.583959e-03  8.978121e-03\n",
       "1994 -5.728447e-03 -4.429988e-03\n",
       "1995  1.262256e-03 -4.247544e-03\n",
       "1996 -2.411027e-03 -3.324094e-03\n",
       "1997 -7.774228e-03 -7.123873e-03\n",
       "1998  1.532642e-03  3.509785e-04\n",
       "1999  4.978202e-04  2.019487e-02\n",
       "2000 -9.208395e-03  1.018698e-02\n",
       "2001 -6.025733e-03 -1.369598e-02\n",
       "2002 -4.155371e-03 -1.085584e-03\n",
       "2003 -7.341315e-03 -1.437916e-05\n",
       "2004  4.283347e-03 -2.577698e-03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepSRresidualsTS <- ts(stepSR$residuals,\n",
    "                        frequency=12,\n",
    "                        start = c(1991,1),\n",
    "                        end = c(2004, 12))\n",
    "stepSRresidualsTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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61bt8LCQi1HAgCgab6+vr6+vlxHoVUl\nJSVZWVmNuXMaAGiCto+x8/T03LdvX2JiIkVRP/74I924b9++lpb7AAB46ciRIzNnzuQ6CoCW\nS9tb7JYvXz58+HBLS8tff/31gw8+2Lx5s1KpvHr16okTJ7QcCQAAqJ1SqaQoiusoAFoubRd2\ngwcPLiwsNDMzMzMzu3z58tGjR2tra8PDw52cnLQcCQAAAADPcHCBYuaOOg4ODtOmTWvkWjU1\nNffu3avfrlAo1BYZAAAAgD7TiTtPZGdn79+/Pzo6WsUy0dHRL1oAN8YGAGhQZmbm6dOncc0B\ngJZDJwq7goKClxZ2ixcvnjRpUv32Hj162NnZaSoyAADdIJePzM8fyZxsKhCQiAgSHv7X0+3b\nyd695OrV/3v4sPWZM//0nj9/fteuXWLxgr17/xmqwXXV1fvaa87MU/WOjF706nuvVJrw4IGM\naJhOFHbh4eHhzKt/AVNTU3d39/rtBgYGAoFAM3EBAOgKA4NCY+PcgIDXmZY2bf7pdXQkAQHk\nzz9vODi4BQS4NdjLprleA4NnnMyLXvTqfu/vv+e0atWOaJhA+6cvSaXShIQEiUTCXLRz7Nix\nzIF3r8rR0dHOzi4rK0vFMiEhIaGhoZGRkU2bAgA0qn379itXrhw3bhzXgei0puW6devWbdq0\n6erVq5oP8C9bt25duXJlXl6e1mYE0BfayXXaPjotNTXVzc0tKSmJoigPDw+hUJicnOzm5nb2\n7FktRwIAAADAM9reFTt//vx169ZNnDiR3ZicnDx37tyMjAwtBwMAAADAJ9reYieRSMLCwuo0\nhoWFSSQSLUcCAAAAwDPaLux69OgRFRVVUVHBtMjl8qioqODgYC1HAgAt2fjx45lEhMthAgBv\naLuwi4uLy8zMFIvFPj4+vXr18vX1FYvFaWlpW7du1XIkANCS7d69u6qqin4sFovv3r3LbTwA\n8PjxY65D4ANtH2Pn6up64cKF7OzsvLw85qxYPz8/LYcBAACgm6RSKUVRLe0Srbm5uX5+fjKZ\nzMTEhOtY9Bs317Hz9fX19fXlZGoAAABdtmTJktra2vj4eK4D0Sq5XF5TU1NTU4PCrpl04gLF\nAAAAQKupqamtreU6CtBXKOwAoIWKj48XiUSEkKqqqp07d7Zu3ZpunzVrVvMHj4iIeFHX2rVr\nmz8+AECDUNgBQEvk5+e3b98++rGXl9eBAweYLrUUdv7+/p9++qm9vf1bb73V/NEAABoJhR0A\ntESZmZkaHX/y5MkSiaSkpATb5wBAm7R9uRMAgBZi6NChgYGBXEcB8HJbt26NjY3lOgpQDxR2\nANBCXb58OSoqin68a9euYcOGpaenq3F8Pz+/KVOmqHFAAA3Jysq6ePGi5sZPS0t78uSJ5sYH\nNhR2ANASpaWl9ejR4+bNm/TTgIAAU1PTkJCQX375hdvAdFlmZiZFUVxHAfpnypQpR48e5TqK\nlgLH2AFASxQVFbV48eKlS5fST729vffs2ePq6hodHa250x2ys7P3798fHR2tYpnz58+npKTU\nby8vLzc3N9dQYI0hk8n8/f2vXbvm7e3NYRigjyiKUiqVXEfRUqCwA4CW6MqVK/UvADtmzJhN\nmzZpbtKCgoKXFnaFhYUZGRn12zm/thk9e50YIiMjO3fuPHr0aI6CAoC6UNgBQEvU4NXtDQ0N\nNbqrMTw8PDw8XPUyw4YNGzZsWP12R0dHS0vL5sdQVVU1dOjQXbt2Mdfta44//vgDW2IAdIre\nFHZ//PFHcnJy/fby8nL6EqMAAI3n4+Nz7NgxLy8vduOhQ4c6d+6srimkUmlCQoJEImHuiz12\n7FixWKyu8ZumvLz82LFjhYWFainsAEDX6E1hl5+f/6LdE9XV1dqPBwD02rJlywYMGGBhYTFx\n4kQjI6Pq6urt27cvW7bs4MGDahk/NTV16NChvr6+3bp18/DwKCsrS05OXrJkybFjx3r37q2W\nKfRIdXW1sbEx11EAtAh6U9iNGjVq1KhR9dsdHR2trKy0Hw8A6LWQkJDExMQZM2bMmDHD3t6+\nqKiodevW+/bt69+/v1rGnz9//rp16yZOnMhuTE5Onjt3boO/UXmsurra1tb28uXLnp6eXMei\nQe7u7j/99FNAQADXgWjQxYsXO3fuzO1JPPBSuNwJALRQ7733Xn5+flZW1vr16zMyMu7duzdo\n0CB1DS6RSMLCwuo0hoWFSSQSdU2hL6qrqysrK8vLy7kORLMePnwolUq5jkKzhg0bduTIEa6j\ngJdAYQcALZeRkVGXLl0GDx7s6+trbGwslUq///57tYzco0ePqKioiooKpkUul0dFRQUHB6tl\nfADtUygUCoWC6yjgJfRmVywAgIbU1tYeP358+/btR44csbW1nTNnTvPHjIuLGzNmjFgs7tix\no5WVlUwmu3Xrlre3t7qO4QMAaBAKOwBoua5evbpz586EhITi4uJJkyadOHGiT58+ahnZ1dX1\nwoUL2dnZeXl5zFmxfn5+ahkcAOBF6hZ248eP37hxI30BEYVCYWBgwEVUAACatX79+h07dmRn\nZw8cODA+Pn7KlCkxMTH29vbqncXX19fX11e9YwIAqFD3GLvdu3dXVVXRj8Vi8d27d7UeEgCA\nxs2ePdva2rqgoCA5OTk8PBw/YgH4rbi4OCcnh+sotAEnTwBAS7Rw4cKcnJygoKDly5ffv3+f\n63AAQLPi4uL++9//ch2FNqCwA4CWKCYm5t69exs3bszOzu7YsWNxcfHevXsfP37MdVwAoMrV\nq1dPnDjRhBWVSmULuf0dCjsAaKEMDAwGDhyYnJxcUFDwzTffbNu2rV27di+9lysAcGjfvn2r\nV6/mOgqd1sBZsfHx8fTJE1VVVTt37mTuJzhr1iythgYAoBV2dnYff/zxxx9/fPHixW3btnEd\nDgDoNIVC8dNPP40cOVIgEHAdSwPqFnZ+fn779u2jH3t5eR04cIDpQmEHAPwWFBQUFBTEdRQA\noNPy8/NHjx7dp0+fdu3acR1LA+oWdpmZmVqOIDg4+PDhw23bttXyvAAAAACviqIo5v86SNsX\nKF6xYkWdlpycnP/7v/+zsrL6/PPPtRwMAADol9zcXHNzcxcXF64DAdBRDZw8cfny5aioKPrx\nrl27hg0blp6erq75fvvttyVLlqSmpl76W21t7ZUrVy5duqSuKQAAgK8iIyPVdT9faL4dO3Z8\n+eWXXEcB/1K3sEtLS+vRo8fNmzfppwEBAaampiEhIb/88ota5jt58mRsbOyDBw/mzZt34MCB\nAwcOWFpabt26lX0wHwAAACFkxYoVZWVl7JaWc9EKvYDtMjqobmEXFRW1ePHivXv30k+9vb33\n7NmzYMGC6OhotcwnEAgWLFjwww8/fPjhh5GRkbW1tWoZFgDglQhejOvQ4C9VVVVLlizJzc3l\nOhAAfVK3sLty5crIkSPrNI4ZM+bKlStqnDUwMPDixYv379/v1avXs2fP1DgyAEBj3H+B69ev\ncx0aAPwlJCTkyJEjXEehZ+qePGFiYtLAQoaGaj/7QyQSbd++PTEx8fjx42ZmZuodHABANUdH\nR/pBTk7O06dP6cdyuXzMmDElJSXcxQUcCwwMTEhI6NSpE9eBACGEPHz4sKioiOso9Ezdws7H\nx+fYsWNeXl7sxkOHDnXu3FkT03/wwQcffPCBJkYGAHipyMjIVatWmZqaGhkZmZmZ3b9//5NP\nPuE6KOBSZmbmo0ePUNiB/qpb2C1btmzAgAEWFhYTJ040MjKqrq7evn37smXLDh48qLkgsrOz\n9+/fr/owvjVr1nzxxRf128vKyoRC3BgNAJoiPj7+1KlTVlZW69evj4+PX7NmDQ7MBwC9Vrew\nCwkJSUxMnDFjxowZM+zt7YuKilq3br1v377+/ftrLoiCgoKXFnZDhw5ldp2wzZgxw9LSUmOh\nAQCfPX36tFu3bpaWlvShdREREV27dp03b55aBpdKpQkJCRKJpLi4uHXr1p6enmPHjhWLxWoZ\nHACgQQ1coPi9997Lz8+/ceNGXl6em5tbp06djI2NNRpEeHj4S2+87ezs7OzsXL997ty5RkZG\nmokLAHjO09Pzhx9+mDFjBiHkzp07lpaWDx8+VMvIqampQ4cO9fX17datm4eHR1lZWXJy8pIl\nS44dO9a7d2+1TAFckUqlKSkpkydP5joQgAY0fOcJIyOjLl26dOnShX4qlUr37t07Z84ctUyJ\nX7EAoCO++OKLkSNHhoaGjhw5MigoyMTEJCwsTC0jz58/f926dRMnTmQ3Jicnz507NyMjQy1T\nAFfOnz8/b948FHagm1QdnVZbW5uSkjJ06FAHB4evvvpKLfOlpqa6ubklJSVRFOXh4SEUCpOT\nk93c3M6ePauW8QEAGm/QoEGPHz92cXGJiIjYtWvXypUrt2/frpaRJRJJ/RoxLCxMIpGoZXzg\nkM7eJBSAvGiL3dWrV3fu3JmQkFBcXDxp0qQTJ0706dNHLfPhVywA6BRzc3P6wYABA9Q4bI8e\nPaKiolatWiUSiegWuVz+xRdfBAcHq3EWgJd68uSJjY0N11Hw36ZNm9544w1fX1+uA6m3xW79\n+vVBQUEBAQH5+fnx8fF2dnYxMTF9+/Y1MDBQy3z4FQsAusDIyGjXrl1GDVHL+HFxcZmZmWKx\n2MfHp1evXr6+vmKxOC0tbevWrWoZH6Axfv/99wYPTwe127Rp088//8x1FITU32I3e/bsfv36\nFRQUtG3blhCirnqOgV+xAKAL8vLyxGLx22+/raHxXV1dL1y4kJ2dnZeXxxxP7Ofnp6Hp4KVy\ncnLMzMzc3Ny4DkSrKisr5XI511GAVtUt7BYuXLhr166goKCpU6dOmTJF7fPFxcWNGTNGLBZ3\n7NjRyspKJpPdunXL29tbo9fJAwCow8XFhRBy6tSpIUOGsNuTkpKGDRumrll8fX2bsGvmyZMn\n9RspilIoFA12MWpra589e8ZeRi6X11mLvs1GeXk5u5GiqPo3ya3T2OCKNTU1z58/rzOjUqlk\nt1RWVjZtxqqqKkKITCarM2NVVVWd96H+aPVblixZYm9vv3LlStWL1Z+x/jIVFRUURdX/LCoq\nKl71NTbYUlVVVf+zbsyK9RtlMhmp941qcMbq6uqXLqZUKisrK9mLPX/+vKampgkr1l+svLyc\nEPL06dOamhr2inK5vAkzXrhwwcvLq3Xr1uwVa2trX7piSkpK165d6fygOtSysrJWrVoxjQqF\nos5fX33aOTpTUH8ahUJx/Pjxbdu2HT9+vLq6+ptvvpkwYQL73Wk+Nf6KdXR0tLOzy8rKUrFM\nSEhIaGhoZGRkk2cBAM1p3779ypUrx40bp81Jf//9d0JIeHh4SkoK0yiTyYYNG1ZWVqahSRtz\nMfaoqKjly5drKAAA4NaiRYvq/LpQuwZOnjAwMBg4cODAgQNLSkp27969bdu2hQsX9u/fn53+\nmqlpv2IBANRl1KhRhJDS0lL6AYO+pp2GNOZi7PPmzWvwup7vvvuutbX1vn37VKxbWloqEonY\nVx6tqakpKyurcz2pwsJC+mAbxowZM/z9/T/88EOmJSkp6Ycffti/f7/qFZ8+fWpqampqasq0\nKBSK0tJSOzs71SsuWbLE3d39P//5D9Ny9OjRjRs31rnje/0Vy8vLDQ0N2XcYP3ny5OrVq0+c\nOMG0KJXK4ODgLVu2dOvWTcWKp0+fXrVqVWpqap0Z7e3t2ZtnPv/8c3Nz88WLF7PHl0qlbdq0\nYa9YVFTUpk0b9orLli0zMDBYunQp03Lu3LnPPvuszlUgQkJCVqxYwb66Ib1F0MLCgmn53//+\n98knn1y4cIG94ltvvbVs2bI333yTafnqq68qKytXrFjBtFAUVVRUVOc9DA0NXbhwYb9+/ZgW\nuVxeW1vLvtp/ZmbmtGnT/vjjD/a9nYqLi8ViMbtFLpfX1NRYWVkxLVevXp08efKFCxfY38OS\nkhJbW1v2wV3ff/99fn7+mjVr2IHV/7jrr/j8+fPnz59bW1szLTdv3hwzZszPP//Mjl8qlVpb\nWxsa/lPkVFVVyeVy9nkk+fn5H3zwwalTp2xtbdkrWllZsY+13bp16//+97/4+HjVoT5+/NjS\n0lL1Qbrvvffea6+9pmIBtWj4rFianZ3dxx9//PHHH1+8eHHbtm2aDgUAQGvoEx0he/UAACAA\nSURBVLZEIhF96JV2Jm3MxdgtLS0DAgLqtxsbG5ubmzfY1XwWFhbt2rVjD37x4kVTU1MNTUcI\nSUlJEQqF7BIhJyfH2Ni4CTPevHnTyMiIvaJCoSCEeHl5qR4tPz/f0NDwpTPa2tq+6ENRrXXr\n1nXGLywsFAqFdYYSCoUeHh6qx5dKpQKBoM4yRkZGnp6e7EY7OzsTE5OXhmpoaOju7q56MfrI\nPH9//1c91J7ekdqtWzd2uV+fvb3948eP1fIFo6s3Pz+/Vz35l96R6uvrW6dGr+P48eMikUgt\noRoZGan91IX6VBV2jKCgoKCgIE2HAgCgZTY2NqdOnRo8eLAmBteji7EbGBho4d8bNvamFGia\nw4cP+/v7cx0F6Bz8aQFAy7Vx48bvvvvOzc3Ny8uL2YeilhJHv24ptnHjxjpbO3r37k2fuwBN\nFhYWxt4kqXY9e/bU3OCaNmjQIFwNQ0NQ2AFAyzV58mSZTFbn/C21nLmmXxdj9/DwqNPi7e3t\n7e3NSTC8MXLkSK5D0F16XZXqOBR2ANByXbt2rU4LfXmI5nvRxdinTp2qlvFBa0xMTExMTLiO\nAqCxUNgBQMtlb2+fk5NDX6GNECKXy8eMGVNSUtL8kXExdt5Ys2aNlg9ABGgOFHYA0HJFRkau\nWrXK1NTUyMjIzMzs/v37n3zyiVpGxsXYeYN9LQ8A3YfCDgBarvj4+FOnTllZWa1fvz4+Pn7N\nmjVKpVItI+OWYgDAiRZa2F2/fv3Jkyc9evTgKoDy8vJu3bqlp6fXuYwnAGjT06dPu3XrZmlp\nef36dUJIRERE165d582bp67xcTF2ANAyDZ6Jrcvi4+NjY2M5DEAmk+Xn5zNH9gAAJzw9PX/4\n4Qf6bgF37twpKyt7+PAh10EB1FX/nrCa5uXl9d///hcHF+ojvdli9/Dhw/Pnz9dvp2/r24QB\ntXMvXgDQZV988cXIkSNDQ0NHjhwZFBRkYmJS/1RWAM4FBwfHxcVpc8Y2bdrUud8X6Au9Kez2\n7t371Vdf1W8vKyuj7wrSTFeuXNm4cePGjRubP1STxcbGDhs2rEOHDhzGANCiDBo06PHjxyYm\nJhERER07dpRKpbj2GGiOra1t69atm7CitbX1pEmT1B0O8JPe7IqdP39+aUPatm2rllv05OTk\nHDp0qPnjNMf333+fnp7ObQwALUpRUZGpqamhoaFAIBgwYMD48ePZdy4HUK/u3bvfuXOH6yiA\n5/SmsAMAUKOsrCw3N7e2bdva2dmdPHmS63CgpdD+0XLQTEZGRsz9BvUCCjsAaIn++9//+vv7\nX758eezYsZMnT8ZBtwBqYWhoKBQKNXqTXC2bNWvWzp07uY7iFejNMXYvJlQoLJ88+ec5+07W\nSiUpKyO1tRbPnpnSy9TvpVVWGiuV/7oK5ZMnZSNHfrhjxw7mGL4XrdukXiEhNmVlQtVRaWBe\n9KJX53opSkS0LiMjIzU1tVu3bkuWLFm/fn1hYWG7du20HwY0n4uLi7+/P9dRwF8CAgIyMjL4\ndEiDSCRi7h+jHyg9Z26+nRCK/d/Klf/0zppFNdg7b968999//0W9tHHjnjZh5Ob0Ojo67t69\nWxMjoxe9Ot67aFEqpV2EkJKSEvqxlZXVnTt3tBzAq3JwcPD19eU6Cg3auXOns7OzWoaiL5Vw\n5swZ1YslJiba2dmpZcbmEIlEKSkpahlq+vTpo0ePfulidnZ2iYmJaplRF2RlZRFCSktLX3XF\nnJwcQkhRUZEmomqQk5NTQkKCpmcRUHq+A6JdO29LS9+9e/cyLV5ehKmtS0uJREKmTZv2+uuv\n0/fepns/+eSTvLy87dsPSSR/LXny5MnVq1dLJCeYdTMyJIGBI44dO9amTZsXjcymore6urq6\n+mqfPgFM7++/F4eFhSUnJzs7OzPrOjk5rVq1asCAsY0fGb3o5UHve+8NWLVq3MSJY4kWCQQC\nqVRKn6JoY2OTlZXl4uKizQBelaOjo52dHf1vGC/t2rUrMjLy7t27zR9KoVAYGhqeOXOmd+/e\nKhb76aefZs2aVVxc3PwZm8PCwuKHH34YOHBg84eaMWNGeXk5+x/EBrVp02b9+vUjRoxo/oy6\nIDs728/Pr7S01Ia9O6ARcnNzvb29i4qKmH/lNa19+/YrV64cN26cRmfR+12xQuHTVq1yAwIa\n7rW1Jba2RCS64eDgXH8Zupd282apkdEV9tZWKysFIRk+PtVOTi8c+UXq9KaknJw8ebJUKmV6\nu3atISSjS5eqjh2bNTJ60cuDXiOjawYGHPzCjI+Pp/ewVFVV7dy5k7kOxaxZs7QfDIDWCAQC\nnMPBY3pf2OmghISE7Ozsb775hmlRKBQKhaIJQ23YsMHe3n7YsGHqiw4ACCEkMDBw//799GNv\nb++UlBSmC4Ud8NvatWvffPNNrqMATUFhp343bty4cuWKWoZKTU11dXVFYQcaNWnSpPHjx7/9\n9ttcB6JVFy9e5DoEAG588MEHXIcAGsSfE5JB9ymVSq5DgAb8/vvvt27dav44SqXSycnp+vXr\nzR8KAJpg2rRpERERXEcBHENhB1qSmJjYq1cvrqPgj0WLFtW5k2NycvL06dO5iocQolQqHzx4\n8PjxYw5jAGjJAgICunfvznUUwDHsitV7d+7cWb58+fbt27kO5CVKS0ufsK832ALExMR06NBB\nLaeeRURETJ48mX2xrry8PLlczl7m1q1b2dnZzZ8L1ELFhpO1a9dqMxJ9YWhoaGBgwHUUAHqP\ngy12Uql09erVc+bMGTVq1KxZs7777jvmdFFogps3b9Y/uT0tLa1pp2uAGp09e/bSpUtqGerw\n4cPXrl1Ty1CgHf7+/vv27fvll1+4DkRvDBkyhH0KS8uBc1RBvbS9xS41NXXo0KG+vr7dunXz\n8PAoKytLTk5esmTJsWPHVF9wSEcMGTIkKirK19eX60BUKSsr69ev39WrV7t06fJKK1ZWVhYX\nF7u5uWkoMD1VU1OTn5/v5eWlerHPP/988uTJHh4erzp+bW2toSFvt51nZGS4uLiIxWKuA9G2\nyZMnSySSkpISbJ9rpFatWnl7e3MdBQcSEhJCQkK4jgL4Q9tb7ObPn79u3bpz586tXbv2yy+/\nXLt27W+//bZz5865c+dqOZKm+e233/Lz87mO4iXocxSasMVu27Zto0aN0kBEavbw4UNtfgpH\njx596623XrrYli1bMjMzX3Xw8+fP62klrVQqG3Ox3OnTp7/0cql8NXTo0MDAQK6jAF03aNAg\nCwsLrqMA/tB2YSeRSMLCwuo0hoWFSepcnF5/1NTU8OZkz5qaGvpWPDouNjZ28eLFWptOo2/L\nkydPnj59qqHBCSGVlZXspwqFYsKECWo5+CEjIyMgIKCmpkb1YkqlssUeFeDn5zdlyhSuowCA\nlkXbhV2PHj2ioqIqKiqYFrlcHhUVFRwcrOVI1GXMmDFff/211qaTyWRqueVOc3z11Vfl5eUc\nBtDkCz63NBcvXnR0dGS3yOXyhISEJnyFampq6pSDtbW1SqWSN79qAAD4QduFXVxcXGZmplgs\n9vHx6dWrl6+vr1gsTktL27p1q5YjUZfy8nJtVjkbN26cOHGi1qar7/nz559//nlubm7zh6qs\nrFy1apW+361Yl8lkMplMppahduzY8d5776llqJYsOzs7MjKS6ygAgM+0fci2q6vrhQsXsrOz\n8/LyiouLW7du7enp6efnp+Uw9Je+7C2Njo6urq6Ojo5Wscyff/65aNGimTNncnh8SXFxsYWF\nRatWrbgKoL6KigovL69Lly61a9eO61j+8ezZs+fPn3Mdhd4rKCjYv3+/6r8LaD6hUNipUyet\n3dkdQKdwcy6er6+vjp9Yyj8TJkyYMmVKnz59tDPd3bt3q6urtTNXc0yYMCEkJESbR+y9VGVl\n5cOHD58+fapThR2oRXh4eHh4uOplLly4cPjw4frt5eXlIpFIM3HxjUAgUMteBQB9pBMXWcjO\nzn7pr9j9+/dv2rSpfntxcfGTJ09CQ0NVrHv16tWHDx+eOXOGabl58+azZ8/YaxUVFZWWlrJb\n6Ku/jhkzxsTEhGmsqKhYvnz5hg0bmJbMzMxbt2798ccfTMvt27fLysrYQ5WUlFRUVLBbqqqq\nCCGTJk0yMzNjGqVSaUxMzI4dO5iW7Ozsy5cvsy9gJpFIHj9+zB6qtLS0traW3UIfzz59+nT2\nlrALFy5kZGQ4ODgwLY8ePTIwMGD/qC0oKHj06BF7qOrq6tu3b3fq1IlpoY+pmjNnjpWVFTsq\nW1tbdktubi5FUeyhHjx4cO/ePXYLvQt70KBB7KuS/vnnn25ubuz3/O7du1ZWVtbW1kzLjRs3\nqqqq2ENVVFQUFRV16NCBaamtrb158+Zrr70mFArZKzo7O7O3z2VkZNy6dYt9sbHHjx8/e/bM\nycmJaSkuLq7zgSoUihs3bnh5ebEjLysr+/LLLzdv3sy0ZGVl5eTkXL58mWkpLS2trKxs3749\n0yKVSut8FemCeMqUKebm5uwYYmNjExISmJYrV66YmJiw79919+7d4uJi9lBPnjyp8ynQxybO\nnDnT0tKSabx3797333+flJTEtBQWFgoEAnt7e/YyDx48YA9VVlZGCAkLC2PeYXqv+scff8z+\nsPLy8uLi4o4dO0ZeTCqVPnjwQMUCekoqlSYkJEgkEmbvxNixY1965ZeHDx9mZGTUbxcKhew/\nMQCABulEYdeY3RMODg4BAQH127OyskQiUYNdDEtLSxsbG3Y+dXR0fPbsGfsyExUVFe3bt/fx\n8WFaFAqFqalpYGAg+9KRhoaGHTt2ZFdj1tbWFhYW7PKoffv2MpmMXWTI5fJ27dqxN1JSFGVk\nZOTv78+uDIyNjV1dXdnVmK2trampKXvLjYuLS2lpqaenJ9Py/PlzsVjcrVs39ks2MDDo1q0b\n++popqam7du3Z/9znpKSIhQK2W+dm5tbcXHxa6+9xrTcv3//zJkzY8eOZb8JAoGga9euxsbG\nTMuVK1fc3d3Z90Wwt7enKIpdwXh4eDx48KBz585MS01NjYWFRZ3rQaSlpfXq1cvFxYVpyc3N\nrfPpt2vXrrq62tXVlWm5du1abm4u+87WUqn0119/HTZsGPvD+u23315//XX2R2NlZWVlZWVn\nZ8e0/Prrr+Xl5ezpKisrHR0du3btyrSUlZX98ssv77//PvsfWiMjow4dOrCrMRsbG3Nzc3Z5\ndO7cOalUyh782bNn9vb27KMRKIoyNDSs890wMTFxcXFhfzdat25tbGzMrtRdXV2lUin7entV\nVVW2trbsz4UQIhQKfX19jYyMmJZWrVo5OjqyX8uRI0fqfDfc3d0LCwvZJX51dbWlpWWdj08g\nEPj4+LC/G/Q7YGNjQ17s999/Z39M/NDka3YOHz58+PDh9duHDRvG/r0BANAwSs8NHTp0zpw5\nXEehl1asWPHdd9+pXobezFlbW6t6sYyMjNLSUrVEJRQK09LS2C2vv/56TEyM6rV27Njh4uLC\nbsnJySGEFBUVsRvNzc2PHDmieqhPP/00LCxM9TL01XkkEonqxepbtmzZW2+99aprad/48eOn\nTJmitemcnJwSEhK0Np12dO3adceOHXUak5KS/P39mzYgcp3aJSYm2tnZcR0FNFdpaemECRNq\nampedcUG/5nQKO3kOg622DVt9wSonRoPLKuzTUgXODs7z549u86GIs5v3WNkZMTeVAY89qJr\ndk6dOpWTeKA+c3Nz9iZ20FM2NjY7d+5swooikahVq1Y6dfKcWmj7ciepqalubm5JSUkURXl4\neAiFwuTkZDc3t7Nnz2o5EtBN6qq9RCLR2rVr61RR27Zt69WrV/MHb7I5c+Zs27aNwwBAa/h3\nzU7+GTBgQGPungJ85ezs/PTpU/7d9kPbW+zoW4rVuRJbcnLy3LlzGzxeGFqaHTt2BAUFaWjw\nESNGqGUcY2NjgUDAPpKskSwsLPiXRKBBcXFxY8aMEYvFHTt2tLKykslkt27d8vb2PnjwINeh\nwV8EAgHOR2nhmpDGdZ+2CzvsngDVxo0b14S1TExM2CfSapqDg0N+fj77xAWAOnDNTgDghLYL\nO3r3xKpVq5gLMsnl8i+++AK7J3RTp06dFi1axD49UzcNHz48JCRELUP179+fferui7DPyeWf\n6dOn6/6HrheYa3aGh4enpKRwHQ4A8J+2CzvsntAvYrF45cqV3MYgEAjY16JrkKGhobq2n/Xt\n21ct4+i1nj17ch0C35w7d47rEACgRcAtxUDXxcbG8nvzGAAAgLrglmKg61RfzRVAL3z//fdc\nhwAALYK2L3cCANACjR8/nusQAKBFQGEHAAAAwBMo7AAAAAB4AoUdAAAAAE+gsAMAAADgCW7O\nilWj8nLna9fe2Lz5n5bQUOLm9tfja9fIhQv/Wh696EWvrvVWVo6Sy/91V1+oD7kOvejV917t\n5Dq9L+wePgx+8KDfnTv/tAiFhLk/2cGDZPv2fy2PXvSiV9d6ZbJZjx79SUAl5Dr0olffe7WT\n6wQURWl6Do0aNmyYk5PTd999x3UgANBE7du3X7lyZdNuE9xyINcB6Dvt5DocYwcAAADAEyjs\nAAAAAHhC74+xI4Rcu3ZtM/uI4nrOnTtXWVlpZmamtZDUpbS01MjIyMLCgutAXllZWRlFUdbW\n1lwH8soqKiqqqqpat27NdSCvTC6Xt2rVKiQkhOtAXlllZSXXIegHdq6jKGr//v329vYCgYDb\nqJrm8ePHJiYmIpGI60CaoqampqSkxMHBgetAmujRo0disdjISC/PWKqoqLC2tu7evTvXgTSF\ndnKd3hd2gYGB8fHxq1atUrFMQUGBUqk0MDDQWlTqUltbKxAI9DFyhUJBCNHTyCmKMjTUvz8N\nhUIhEAj+97//cR3IK7Ozs/P09OQ6Cl1XJ9fV1tYWFBQYGhrqaWGnv8mNEKJUKhUKhZ4WRoSQ\nmpoaAwMDoVAvd9kpFAoDA4MzZ85wHUhTaCfX6f3JE40REhISGhoaGRnJdSCvbMiQIa6urqtX\nr+Y6kFc2derU6urqXbt2cR3IK1u4cOG1a9eOHj3KdSCvbNWqVQcOHEhPT+c6ENCGhw8fOjo6\n3rhxQ09r4oEDB3bu3Dk2NpbrQJri4MGDkydPfvLkCdeBNFGbNm3Wr18/YsQIrgNpis8///zS\npUsnT57kOhDdpZcFOwAAAADUh8IOAAAAgCdQ2AEAAADwBAo7AAAAAJ5AYQcAAADAEyjsAAAA\nAHhC/y7W1QTvvvtuQEAA11E0xdtvv21vb891FE0REhJSU1PDdRRN0b1797Zt23IdRVMEBgZW\nVVVxHQVoiY2NzYgRI/T0u0oI6devn4uLC9dRNFGnTp309FohtOHDh3fu3JnrKJrojTfesLW1\n5ToKndYirmMHAAAA0BJgVywAAAAAT6CwAwAAAOAJFHYAAAAAPIHCDgAAAIAnUNgBAAAA8AQK\nOwAAAACeQGEHAAAAwBMo7AAAAAB4AoUdAAAAAE+gsAMAAADgCZ4Xdnfv3h0wYICVlZWfn9/p\n06e5DqexduzYERERwTzVi1eRk5PTt29fa2trZ2fn6Oho+lZ1ehF5XFxcx44dW7Vq5eLisnz5\ncrpRLyKnURQVGhq6YsUK+qkeRQ5Npr+fsj4mN6LP+Y2m71mOING9EorXunfvPnPmzKKiom3b\ntolEIqlUynVEL5GZmbls2TKxWDx79mymUfdfhUwms7W1jYyMLC0t/f33352cnNavX0/pQ+RZ\nWVmmpqY///yzTCZLS0szMTE5duwYpQ+RM2JjYwkhX375Jf1UjyKHJtPHT1lPkxulz/mNxoMs\nRyHRvQo+F3ZZWVkmJibl5eX00x49evzf//0ftyG9VHx8/IwZM7y9vZncpxevIjU1VSQSKRQK\n+unSpUsHDx6sF5Hfu3fv1KlTFEVVVVXl5OTY2dldvHhRLyKnXbp0ycPDo0+fPnS+06PIocn0\n9FPW0+RG6XN+o+l7lqOQ6F4Rn3fF5uXldezY0cLCgn7q7+8vkUi4DemlpkyZsnHjxrfeeotp\n0YtX8cYbb+Tk5AiFQkKIUqk8e/Zsjx499CJyJyen0NDQx48ft2rVytvbe+7cuYGBgXoROSGk\noqJi3Lhx27Zts7GxoVv0JXJoDj39lPU0uRF9zm80vc5yBInu1fG5sCsuLraysmKeWllZFRcX\ncxhP0+jFqxCJRM7OzoSQ/Pz8sLAwoVA4bdo0vYic1rp165qamosXL65du/bQoUP6EnlERMTw\n4cN79+7NtOhL5NAcvPmU9eWF6Ht+o+lpliNIdK+Oz4Vd69atZTIZ87SsrMzW1pbDeJpGX15F\ndXX10qVLe/bs2adPn5MnT1pbW+tF5Ldv37558yYhRCgUBgYGjhw58tixY3oReWJiYm5u7rJl\ny9iNehE5NBNvPmU9eiF6mt9o+pvlCBJdk/C5sPP09Lx165ZcLqefXr161dPTk9uQmkAvXgVF\nUUOHDr18+fLVq1c/++wzAwMDoieRp6amzp07l3laWVlpYmKiF5EfP348JyenXbt2dnZ2x44d\n++qrr4KDg/Uicmgm3nzK+vJC9De/0fQ3yxEkuqbh+iA/zQoMDFy0aNHz588PHDggEolKSkq4\njqhRZs+ezT5xTPdfxalTp6ytrf/88887fysuLqb0IfK8vDwLC4v9+/eXlZWdOHHCwsLi7Nmz\nlD5EXlpaev9v/fv3X7BgwaNHjyh9iByaT38/Zb1LbpQ+5zea/mY5ComuSXhe2N2/f/+dd96x\ntrb28/P77bffuA6nserkPt1/Fcy1hRiDBw+m9CFyiqJOnTrVo0cPkUjk7e39448/0o16ETlj\n8ODBzFUA9CtyaBr9/ZT1LrlRep7faDzIchQSXaMJKIrS1sZBAAAAANAgPh9jBwAAANCioLAD\nAAAA4AkUdgAAAAA8gcIOAAAAgCdQ2AEAAADwBAo7AAAAAJ5AYQcAAADAEyjsAAAAAHgChR0A\nAAAAT6CwAwAAAOAJFHYAAAAAPIHCDgAAAIAnUNgBAAAA8AQKOwAAAACeQGEHAAAAwBMo7AAA\nAAB4AoUdAAAAAE+gsAMAAADgCRR2AAAAADyBwg4AAACAJ1DYAQAAAPAECjvQCTY2NidPnuQ6\nCgAAzUKuA01DYQcAAADAEyjsQHcdOnTI19fXzMzM0dFx6dKldOPPP//s5+dna2s7adKkjz76\n6Ntvv+U2SACAZkKuAzVCYQc6qrKycuTIkTNmzHj06NHu3btjYmJycnIePHgwaNCgxYsX37lz\nJygoKD4+nuswAQCaBbkO1MuQ6wAAGmZiYpKTk9OhQweKomxsbExNTZ8+fXr8+PG+fft+8MEH\nhJBZs2Zt3LiR6zABAJoFuQ7UC4Ud6CgDA4OjR48mJiYqlcpOnToZGRkRQgoKCtzd3ZllXF1d\nOYsPAEAdkOtAvVDYgY46ceJEdHT0xYsX6Yx26tQpQoiDg0N6ejqzzN27d7kKDwBALZDrQL1w\njB3oioqKijIWqVRqampqaWmpUCi+++67+/fvy+XyDz744PTp08nJyTKZbMOGDTdu3OA6agCA\nV4NcBxqFwg50xfDhw61ZBg0a1Lt3b3d3dx8fn9ra2nnz5o0ePdrOzm7fvn1Llixxc3PLz8/v\n37+/SCTiOnAAgFeAXAcahV2xoBOePHlSv3Hv3r3sp99++61EIqEoKjc3l24JCgqys7PTRnwA\nAOqAXAeahi12oE+qq6uHDx9++fJlQkhKSsrNmzf79u3LdVAAAGqGXAdNhi12oE+8vLy2bt06\ncuTI8vJyZ2fn5ORka2trroMCAFAz5DpoMgFFUVzHAAAAAABqgF2xAAAAADyBwg4AAACAJ1DY\nAQAAAPAECjsAAAAAnkBhBwAAAMATKOwAAAAAeAKFHQAAAABPoLADAAAA4AkUdgAAAAA8gcIO\nAAAAgCdQ2AEAAADwBAo7AAAAAJ5AYQcAAADAEyjsAAAAAHgChR0AAAAAT6CwAwAAAOAJFHYA\nAAAAPIHCDgAAAIAnUNgBAAAA8AQKOwAAAACeQGEHAAAAwBMo7AAAAAB4AoUdAAAAAE+gsAMA\nAADgCRR2AAAAADyBwg4AAACAJ1DYAQAAAPAECjsAAAAAnkBhBwAAAMATKOwAAAAAeAKFHQAA\nAABPoLADAAAA4AkUdgAAAAA8gcIOAAAAgCdQ2AEAAADwBAo7AAAAAJ5AYQcAAADAEyjsAAAA\nAHgChZ3OGTVqlEAgEAgEKSkp9Xt3794dFhbWrl07Kyur7t27b968WalU0l0+Pj6Chpw8efKV\nAnj06FGvXr1MTEw6duyohtfDnYiICIFAMH78eM1NYWNj86J3WF2zq5gCQPcFBQWx05G5ubm/\nv/+uXbvqL4nUpy5IfS2cIdcBwL/I5fIjR47QjxMTE8PDw5kuiqLGjRu3d+9epiU9PT09Pf34\n8eP79+83MDBQVwxbtmw5f/68mZlZcHCwusYEACCEyOXyzMzMiRMnEkImTJjAbkfqA1ALbLHT\nLSkpKZWVlfTjw4cPV1VVMV0JCQl0ahs/fnx6evqVK1cWLFhACDl48CD75++cOXMq/q1fv36v\nFENJSQkh5P3339+zZ0/zXxG/3b9/vwnvMEBL8+GHH1ZUVMhksqysLG9vb0LIt99+y14AqU+/\nIPXpMhR2umXfvn2EkLCwMKFQWF5efurUKaZr6dKldNeuXbtef/11Hx+f2NjYd955hxBy6NAh\nZjFjY2Pzf2vwF+2PP/7Ys2dPa2trd3f30aNHFxQU0O3du3dft24dHYmFhUWdte7duzdhwgQn\nJydzc3MfH5/Vq1crFAq6q7a2dvHixe7u7m5ubl9//fVHH30kEAgmT55MCHn8+DG9Z+T27dv0\nwuPHjxcIBBEREfRTiUQyatQoR0fHVq1aeXp6Llu2rLa2lu6iVzx37tzMmTODgoIIIdXV1Z99\n9lnnzp1FIlFQUNCPP/7IhKdUKqOiojw8PBwdHT/99FNmT019rzSsilftNRPfXAAAIABJREFU\n5OQkEolOnz6tevbmvAONfP8BdBmdl0Qika+v78yZMwkhN2/epCiKWQCpD6kPqU9tKNAZZWVl\nJiYmhJCkpKRevXoRQsaNG0d30T8lCSFHjhxhr1JeXn7//v2ioiKKorp06ULq/Wx9/vx5/YlW\nrFhBj2ZjY2NoaEgIsbKyunfvHkVR69ev9/f3J4R4e3svXbqUvZZCoaB/apuamjo5OdEjLFmy\nhO6l960QQoyNjQkh7dq1I4RMmjSJoiipVEp35eXl0QuPGzeOEDJ79myKoqqqqry8vAghQqFQ\nLBbTS3755Zf0kvTT/v37E0I8PDwoigoLCyOEGBkZde7cWSAQEEI2btxILzxjxgx2DObm5uz3\nkK3xw6p+1dbW1oSQEydOqJ69Oe8AM4XqSAB0U2BgIPNVp23YsIEQYm1tzbQg9SH1IfWpEQo7\nHbJz5076b0Mmk61atYoQYmlpSaenCxcu0N/mW7duvWh1OrvVMXXq1DqLPXr0qFWrVoSQ6Oho\niqIKCws7d+5MCJk8eTK9wOzZsxtMCrm5ufSYEomEoqj4+HhCiJOTE0VRV69epTPC1q1bFQrF\n/v376SUbk93OnTtHCBGJRI8ePaIo6tNPPyWEvPnmm/SS9IoODg5xcXGnT59OS0uj35bCwkKK\norZu3Uqn5tra2hs3btA/0OPi4hQKRXJyMh2SiuzWmGFVvGqKlXpUz96cd4CZQnUkALqJLuyY\nXbFXrlyhM1VISAizDFIfhdSH1Kc+OHlCh9A7I/r27SsSid5///2FCxfSuyTCw8Opv/8gm3+k\n8KVLl549e2ZnZ/fZZ58RQuzt7T/99NNJkybRf2MqWFlZGRgYKBSKvn37Dhs2LCwsrLq62sjI\niBBy9uxZiqK6du36n//8hxAybNiwXr16vXRAWnBw8NOnTysrK69du7Zt27bExERCSEVFBXuZ\nmJgY+hyr5cuX0zGvWbOGECKXywkhZWVld+7cOXPmjEKh6NSp0/Tp0wkhQ4YM6dOnz6+//qpi\n6sYMq+JVszVh9ld6B4jK9x9Ax23evHnz5s3sFjr/0JD6kPqQ+tQIx9jpitLSUvp4hX79+lVW\nVjo5Obm5uRFC6O86c/o9c6QC7caNG4cOHfrll1+Ylvnz57Mr9y1bttSZiD6mxNnZmUmUHTp0\nIITcvXtXdYQODg6HDx/28/OTSCTffPNN3759HR0dExISCCH37t0jhLi7uzML02M2hlAoXLhw\noaura2hoaExMDL1Hpg4PDw/6AR3krVu3YmJiYmJivv/+e7o9Ly+Pfl3seV8aQ2OGVfGq2Zow\n+yu9A0Tl+w+gL4yMjPz8/JKSkt599126BakPqQ+pT71Q2OmKpKSkmpoaQsj8+fNFIpGFhYVE\nIiF/nyBmZ2dHH1tAbypnfPLJJ4MHD960aVPjJ2rfvj0hpKCggDn4lM6YdLtqYWFhmZmZf/75\n59dff+3v719SUjJ16tTy8nJHR0dCCB0wjf2Y8ezZM/oBc/obIWTv3r2bNm2ytbU9f/58WVkZ\nc1Atm1D41xfV3t6eEDJixIg6W5779+9Pvz8vjeFVh1XxqtlDNXL2Jr8DtMZEAqCDmGPsqqur\nMzMzhw4dynQh9SH1IfWpFwo7XUHvjKiPOUGM3ma+b9++jz/+ODc39/bt24sXLz569CghZNCg\nQY2fKDAw0NTUtKSkJDY2lhBSXFxMP6CPWVZh/fr1FhYWvr6+zs7O8+fPpy9AUF1dXVRURK+b\nnZ29Y8cOiqKSkpLOnj3LrGhpaUn/RN65c+fz58/T09NTU1OZ3uvXrxNC7O3tg4ODnz179qL3\ngUZfX+rMmTP0oRtpaWkdO3bs2rWrTCbr3bu3QCDIycmJj4+nKKrOr3nVVAyr4lWzR1A9u1re\ngUZGAqBfkPqQ+pD61OxVDsgDTSksLKS//Rs2bGAalUolvUuCPgpVoVCMGDGi/ic4ZswYenn6\nCOI6+yMaFB0dTa8rFovpIxWsra3pU8OoFx9BfPv2bUtLS0KIiYmJl5cXvWJQUBDdO2TIEHpM\n+sQo+ocgfQQxRVFvvfUW3Uv/UhSJROTvH/H0DhdCiJWVlUgkohfw9fWlV6S70tPTmfeke/fu\n9MI+Pj70wrGxsXTvqFGj2DHQvSqOIG7MsKpfNfvUMNWzN/kdYKZQHQmAbqp/ViwbUh9SH1Kf\n2qGw0wlr164lhJiamj558oTd/sUXXxDWCWIURW3dujU0NFQsFrdp06Z379579uxRKpV0V+Oz\nG0VRe/bseeONNywtLV1cXOiLOTFdL8puFEVlZGQMHTrU0dHRxMTE2dl52rRp9LlUFEVVVVXN\nmzfPxcXFxcXlq6++ok+AZ7LbnTt33n33XQsLC09Pz2+++Wbs2LHM37ZSqVy6dKmDg4O9vf2k\nSZPouwkZGxtfv36dqpeGKIqqqKiIiIjo0KGDubl5YGDgDz/8wHTV1NQsWrTI3d29TZs2U6dO\nnTJlSiOzm+phVbxqdnZTPXuT3wH2FCoiAdBNqgs7pD6kPqQ+tRNQrEtEAqjLRx99FBcXN2nS\npO3bt3MdCwCAliD1AedwjB0AAAAAT6CwAwAAAOAJFHYAAAAAPIFj7AAAAAB4AlvsAAAAAHgC\nhR0AAAAAT6CwAwAAAOAJFHYAAAAAPIHCDgAAAIAnUNgBAAAA8AQKOwAAAACeQGEHAAAAwBMo\n7AAAAAB4AoUdAAAAAE+gsAMAAADgCRR2AAAAADyBwg4AAACAJ1DYAQAAAPAECjsAAAAAnkBh\nBwAAAMATKOwAAAAAeAKFHQAAAABPoLADAAAA4AkUdgAAAAA8gcIOAAAAgCdQ2AEAAADwBAo7\nAAAAAJ5AYQcAAADAEyjsAAAAAHgChR0AAAAATxhyHUBzJSQkpKSkcB0FADSdUCiMiop67bXX\nuA5EpyHXAeg77eQ6vS/sDh48ePPmzR49enAdCAA00Z49ewYOHIjCTjXkOgB9p51cp/eFHSGk\nb9++3333HddRAEATHTt2jOsQ9ANyHYBe006uwzF2AAAAADyBwg4AAACAJ/RmV2xSUlJcXFz9\n9j/++MPLy0v1umvWrPHz83vzzTc1EhkAAPwtOzs7OTl5+fLlXAcC0ELpTWHXtm3bgICA+u3p\n6elPnz5VvW5ycrJMJkNhBwCgaZcuXdqzZw8KOwCu6E1h17Nnz549e9ZvT0hIMDMz0348AAAA\nALoGx9gBAAAA8AQKOwAAAACe0JtdsQAALcSTJ0/qNz582Onu3TcXLfqnZfhwEhj41+O0NJKa\n+q/lueo1MhIzj3UnKvSiVxd6y8o+ffKkFdEwFHYAADokKirqBWceTDM1NbWx+ef566//889G\nfj7JyPjX0lz1enqaM491Jyr0olcXemtqulZWyoiGCSiK0vQcGuXo6GhnZ5eVlaVimZCQkNDQ\n0MjISK1FBQCN1759+5UrV44bN47rQHRCeXn5rVu36rcPGDCgbdu2V65c0X5Ir2Tr1q0rV67M\ny8vjOhAAnaOdXIctdgAAOsTS0rLBSzsZGRkJhTgqGgBeAmkCAAD45vnz51yHAMANFHYAAMAr\nFEW1adMmOzub60AAOIDCDgAAeEWpVMpksvLycq4DAeAACjsAAAAAnkBhBwAAAMATHJwVm56e\nnpeX9+6775qZma1cuTIrKyssLOyjjz7SfiQAAJojlUoTEhIkEklxcXHr1q09PT3Hjh0rFotf\nviYAQFNpe4vdli1bQkNDZ82a9fbbby9btowQ0rVr18jIyK+//lrLkQAAaE5qaqqbm1tSUhJF\nUR4eHkKhMDk52c3N7ezZs1yHBgB8pu0tdqtWrTp69Ki5uXlgYGB8fHxQUBAhpF+/fh9++OGC\nBQu0HAwAgIbMnz9/3bp1EydOZDcmJ/8/e/cdFsW19wH87NIFdkFAFBRBKSoioGIUFUusREWJ\nisGKFYPYXo2JoqBGRWMSSSwkQY29XIMdCzF2Y4uKgo2OCtJ7Z3feP+bevXMXpKy7O7vD9/Pc\n5z7smZkz32XZk59TzkQuXrz4H6n56QEA5EfZR+zevXvn6OjYpUsXPp/v4uJCN7q6ur5//17J\nSQAAFCc5OdnT01Oq0dPTMzk5mZU8ANBMKLuws7e3P3r06PHjxymKOnbsGN149OhRZ2dnJScB\nAFAcd3f3kJCQkpISSUtZWVlISEivXr1YTAUAnKfsU7Fr164dP368QCC4du3axIkTf/31V7FY\n/OzZs4sXLyo5CQCA4oSHh/v6+pqamtrZ2QmFwuLi4vj4eEdHx1OnTilup7NmzRo0aBCeugvQ\nnCm7sBs7duz79+9btGjRokWLR48enT9/vqamZvTo0W3btlVyEgAAxbG2tr5z505MTExCQoLk\nrljJ9ScKEh8fb21trdBdAICKY2G6E8nd/hYWFnPmzGnkVtXV1W/evKndLhKJ5JYMAECunJ2d\ncZ0JACgTC4VdbTExMSdOnFi/fn0966xfv/5DK/D5mGYZAAAAQDUKu7S0tAYLu5UrV86YMaN2\nu7u7u5mZmaKSAQDIJDAw8EOLfv75Z2UmAYBmRSUKu9GjR48ePbr+dXR1dTt06FC7XUNDg8fj\nKSYXAICMunfv/tVXX5mbmw8aNIjtLADQjLBQ2OExOwDAeX5+fsnJydnZ2U09Ppeamnr//v3a\n7RUVFdXV1XJKBwCcpezCLjo62tvb29nZ2dXV1dbWtrCwMDIyMigoKCoqqn///koOAwCgON7e\n3jI8ZOLUqVNr166t3V5YWNiiRQt55AIALlN2YYfH7ABAM+Hi4iLD/CaLFi1atGhR7XZLS0sT\nExN55AIALlP2/aR4zA4AAACAgii7sMNjdgBAFUydOlUyECltOsyYmJjVq1crZ18A0Dwpu7AL\nDw9//Pixqampk5NTv379nJ2dTU1Nr1y5snv3biUnAYDm7ODBg5WVlfTPpqamqampStgpPbWT\nEnYEAM2Wsq+xY+UxOwAAqqAxUzsBAHwMduaxw2N2AIDzMLUTACgfHsYFACB/0dHRNjY2f/zx\nB0VRtra2fD4/MjLSxsbm5s2bbEcDhQgLC8vJyWE7BYBqPHkCAED5IiIiDAwMCCGVlZX79u2T\nTCYSEBDw8Z1jaqfmZsWKFZ06dRo+fDjbQaC5Q2EHAM2Ri4vL0aNH6Z8dHBxOnjwpWSSXwu5D\nUzvNnj374zsHAPgQFHYA0Bw9fvxYof3TUztt3ryZPihICCkrK1u3bh2mdgIAhUJhBwAgf+Hh\n4b6+vqampnZ2dkKhsLi4OD4+3tHR8dSpU2xHAwAuw80TANBMPXr0KCQkhP55//79n3/++d27\nd+XVOT21071790JCQiZPnrxq1ao7d+48ePDA0tJSXrsAaFBiYuKoUaPYTqGi8vLyPD09JfNZ\ncgaO2AFAc3TlypXPPvvM29ubftmjR49Lly55eHhcunRp0KBB8toLpnYCdqWmpl64cIHtFCrq\n/fv3Fy5cKCwsbNWqFdtZ5AlH7ACgOQoJCVm5cuXhw4fpl46OjocOHVq+fPn69evZDQYA8DFQ\n2AFAc/T06VMfHx+pRl9f36dPn7KSBwBALlDYAUBzpKOjU7tRU1OToijlhwHgDIqinj9/znaK\nZk1trrG7f/9+ZGRk7faioiLJbAIAAI3k5OQUFRXl4ODAbDx9+nSXLl3YigTAAY8ePerVq1d5\nebm2tjbbWZoptSnskpKS6pyuvbq6uqqqSvl5AECtBQcHjxw50tDQcPr06VpaWlVVVXv37g0O\nDsZ0JACEkJqamrt37/br16+pG1ZXV4vFYrFYrIhU0BhqU9hNmjRp0qRJtdstLS2FQqHy8wCA\nWvPw8Dh+/Li/v7+/v7+5uXlmZqaJicnRo0dZfyTU+fPn9+3bV7s9Pz9fT09P+Xmgebp///6A\nAQOqq6v5fFyypWbUprADAJCvzz77LCkp6dWrVwkJCTY2Np07d1aFk0c6OjrGxsa12/l8Po/H\nU34eaJ5EIpFYLMYlp+oIhR0ANF9aWlpdu3bt2rUr/TInJ+fw4cMLFy5kMdKQIUOGDBlSu/3c\nuXP6+vrKzwMA6gWHWAGguaupqTl79qy3t7eFhcXGjRvZjgMAIDsUdgDQfD179mzZsmWWlpZj\nxowRCoUXL1589+4d26EAAGQnXdhNnTq1pKSE/lkkEik9DwCAMuzYscPNza1Hjx5JSUkRERFm\nZmahoaGDBw/W0NBgOxoAgOykC7uDBw9KHohramqampqq9EgAAAq3YMECIyOjtLS0yMjI0aNH\no54DAG7AqVgAaI5WrFgRFxfn5ua2du3at2/fsh0HAEA+UNgBQHMUGhr65s2bXbt2xcTE2NnZ\nZWVlHT58ODc3V467yMnJ+fHHHxcuXDhp0qSAgICwsLCcnBw59q98CQkJbEcAjrh///65c+fY\nTsFNKOwAoJnS0NAYNWpUZGRkWlra1q1b9+zZ06ZNm9GjR8ul8+joaBsbmz/++IOiKFtbWz6f\nHxkZaWNjc/PmTbn0L7PKykovL6+8vLymblhSUmJvb//ixYumbigWi2fMmKHuRa0yrV+/Pjg4\nmO0UinXixInw8HC2U3BTHfPYRURE0E9frays3Ldvn4mJCd0eEBCg1GgAAEphZma2ZMmSJUuW\nPHjwYM+ePXLpc9myZdu3b58+fTqzMTIycvHixXU+HVFpioqKzpw5k5GR0bJlyyZtWF1dTVGU\nDI9wLCsr27dv34IFC0xNTZu6rULFxsZu27YtIiKC7SDSUlJSampq2E6hEqZPnz5r1iwPDw+2\ng0irqKjQ1dVlO0XdpAs7FxeXo0eP0j87ODicPHlSsgiFHQBwm5ubm5ubm1y6Sk5O9vT0lGr0\n9PScPXu2XPqHprp27ZqBgUHPnj0lLS9evDhz5gyLkWgXL17s378/t2efvnbtmouLi5GRUVM3\nvH37toeHh6oVdu/evbO1tU1PT6/zITGskz4V+/jDFJSgV69e79+/V1DnAACscHd3DwkJkcwe\nRQgpKysLCQnp1asXi6masx07dtT5EF7W+fj4XL9+ne0UiuXn58elK+pKS0srKirKy8vZDlI3\nZT9SbMOGDVItcXFxP/zwg1AoXLVqlZLDAAAoSHh4uK+vr6mpqZ2dnVAoLC4ujo+Pd3R0PHXq\nFNvRQLWIxWKxWMx2CsVqDu9RddRR2D169OjMmTMhISGEkP37958+fXr58uW9e/eWy/6uX78e\nHR09YMAAyQHMmpqap0+f6unpyaV/AABVYG1tfefOnZiYmISEhKysLBMTE3t7excXF7ZzAQDH\nSZ+KvXLliru7++vXr+mXPXr00NXV9fDwuHr1qlz2d+nSpS1btrx7927p0qUnT548efKkQCDY\nvXs382I+AAAOyMnJ+euvv64z4M5QAFA06cIuJCRk5cqVhw8fpl86OjoeOnRo+fLl69evl8v+\neDze8uXLjxw5Mnfu3NWrV+PGHwBgBe/D5NK/yk53AgDcJn0q9unTp7Xv/fb19f3ll1/kuNee\nPXs+ePAgMDCwX79+Knv5IQBw2IeeNlFcXCyX/lV2uhPgjKqqKm1tbbZTgMqRPmKno6NTeyVN\nTU2KouS7YwMDg7179y5dunTChAktWrSQb+cAAPWz/I+CgoKU/3j+/Hn//v3l0v+HpjtJTk6W\nS/8A9vb28rpKCrhE+oidk5NTVFSUg4MDs/H06dNdunRRxO4nTpw4ceJERfQMANCg1atXb968\nWVdXV0tLq0WLFm/fvv2///s/ufRMT3eyefNmer53QkhZWdm6desw3QnIS2FhYWFhIdspQOXw\npA7F3bhxY+TIkWFhYdOnT9fS0qqqqtq7d+/ixYtPnTo1fPhwBYWIiYk5ceJE/Zfxbdu2bd26\ndbXbCwsLLSws3rx5U8+2Hh4eQ4cOXb169ccGBQAFaNeu3aZNm6ZMmaL8Xbdp0+bIkSNCoXDH\njh0RERHbtm0Ti8VLly79+J5TUlJ8fX0fPXpUe7oTS0vLejbcsmVLaGho7faCggIejycUCuvZ\ntri4WEtLizkhfnV1dWVlpaS4JIRQFFVQUCAQCDQ0NCSNZWVlGhoazDM2Mm9YVVVVUVEhEAia\numFNTU15ebmhoSHzHRUVFRkYGPD5/z25VF5ezufzG9wwPz/f0NBQU/O/By9KS0t5PB7zBFF1\ndXVZWZnUr7SoqEhfX58Ztby8nMfjMX+rde6xoKDAwMCAucfaG4pEotLSUuYvh95QX19fS0uL\n+cuhKIo5ZXEjN6yoqKAoijnLRE1NTXFxsdQ8usXFxXp6esyoMm9YWVkpEomYv1WRSFRUVCS1\nYWFhoZ6eHvPEcXl5uUgkYv6BicXi4uLi2h+Hrq4uc8Pae6Q3FAgEzAtkS0pKdHR0mL+cD0U1\nMjKqf8Oqqqrq6mrmxyEWiwsLC6U2LC0t1dLSqv/keGFh4cqVK+V108KHSB+x8/DwOH78uL+/\nv7+/v7m5eWZmpomJydGjRxVX1RFC0tLSGizsvL296xwN/f39pf7WAQAaqaCgwNXVVSAQ0I9A\nDQwM7Natm1wKO5mnO5k2bVr37t1rt0+cOFEoFP7222/1bJueni4QCJj/sTx9+vS5c+ektoqP\nj7e1tWX+N2n58uVOTk7Tpk2TtJw7dy4yMlLqAWu1N/z666/t7e1nzpwpaamsrHz//n379u3r\n3zAoKKh9+/Zz5syRtERHR//++++HDh2StFRXV3t6eq5bt455yigkJMTc3Hz+/PmSlqtXr4aH\nhx87doy5x4SEhI4dOzL3mJ2drampySw4qqur3759a2Njw9xw2LBhq1evZn5SGzdu1NfXX7Ro\nkaTl9u3bW7duPX78OHPDxMREa2trZkW4ZcsWDQ0N5jHge/fuffvtt1IbJiUlWVlZMaulvLw8\niqIkz/MkhDx8+HDNmjUNbrhz587S0tLly5dLWsRicVJSkq2tLXPD8ePHL1y4kPk4h+3bt+fn\n5zMPf1AUlZiYKLXhpEmT5s6dO3jwYElLeHh4RkbG2rVrmRsmJCTY2dkxN0xJSWnTpg2zHC8s\nLKyoqDA3N5e0vH79OiAgYN++fcz6MjU1tXXr1swN9+zZ8/r1a+Y/fpKSkubNm7d3717mX/7M\nmTO9vb1HjRoladm/f/+zZ8++++67+qPOnTvX09Nz7NixkpZDhw49fPjwxx9/ZK4WHx8vteH8\n+fOHDBny+eefkw/z9fXt0KFDPSvIRR3z2H322WdJSUmvXr1KSEiwsbHp3Lmzoi/PHD16dIMP\n3raysrKysqrdvnjxYmZlDQDQePb29keOHPH39yeEpKSkCASC9PR0Ofbv7Ozs7OzcpE1at27d\nunXr2u16enpCoXDIkCFN6u3ly5fXrl2T2qp2J8bGxh06dGC2JyQk6OvrN7ihiYmJjY1Ng6lq\nr7Bt27b27dsz29PT03V1dZktlZWVhBA3N7c+ffpIGnfs2GFlZcVcLTs7W0dHp8GojcTj8bp3\n786sXfbs2SMQCJgdlpSUaGpqNrjHAwcOSK1WWVmpoaEhQzaRSMTn8xvc8JNPPhGJRA0+uUtb\nW9vJyYnZ2+nTp7W0tKT6Hzp0qNSGOjo6Xbt2Za4WFRVVU1PT4IaNQcceNGgQsz6r7erVq7m5\nucw9xsTEEEKY8+MSQvT19Tt16sRc7datW2/fvm0wqoGBgYODA3O1u3fvJiQkNPhxGxoa2tvb\n1/8ZSR0LVJC6nzyhpaXVtWvXrl270i9zcnIOHz68cOFCuewyJyfnwIEDycnJkn/FTp48WdUe\nDg0AzcG6det8fHyGDh3q4+Pj5uamo6NT+44HOWrMZScAMpM6OwzNk/RdsUw1NTVnz5719va2\nsLDYuHGjXPaHuZ0AQHV4eXnl5ua2b98+MDBw//79mzZt2rt3r+J2R192orj+gasMDQ1RtEEj\n1X3E7tmzZ/v27Ttw4EBWVtaMGTMuXrw4YMAAuewPczsBgEqRXBM9cuRIRe+rMZedALvkOEm1\nHLm7u6ekpLCdQkWZm5v36dOHeXNDMyd9xG7Hjh1ubm49evRISkqKiIgwMzMLDQ0dPHgw84LQ\nj4G5nQBAFWhpae3fv1+rLvLaRU5Ozo8//rhw4cJJkyYFBASEhYXhkWL1aNu2rb29PdspyL59\n+9zc3NhOUQcULh/SunXrO3fuNHgzAJ/PZ95ezWHSR+wWLFgwZMiQtLQ0+updedVzEpjbCQBU\nQUJCgqmp6aeffqqg/qOjo729vZ2dnV1dXW1tbQsLCyMjI4OCgqKiouQ1BzLHDB48mHnLAiFE\nW1t74cKFUvceKtrkyZPl1dWkSZPk/t9QkNmcOXPGjRvHdgplkC7sVqxYsX//fjc3t9mzZ8+a\nNUvu+wsPD/f19TU1Na09t5Pc9wUA8CH0fByXL1+WGuv/+OOP+icsaCRcdvLxeDxeWFgY2ymI\nkZGRbJNqKeHkPjRemzZt2rRpw3YKZZAu7EJDQzds2HDhwoU9e/bY2dlVVVUdPnx42rRpzAl1\nPobMczsBAMjRvXv3CCHz5s2zsLCQNBYXF8+cOVMuhd2HLjuZPXv2x3cOyrR9+3YVvOoO4EPq\nuHlCQ0Nj1KhRo0aNys7OPnjw4J49e1asWDF8+PCzZ8/Ka68yzO0EACBHkyZNIoTk5eXRP0jQ\nc9p9PFx2whnN5MIs4Iy674qlmZmZLVmyZMmSJQ8ePJCafxwAQK3RN2wZGBjExcUxnzIkL7js\nBABYUV9hJ+Hm5qaadwkBAHwMY2Pjy5cvMx8fJC+47AQAWNGowg4AgJN27doVFhZmY2Pj4OAg\nmehEjncysn7ZibOz84gRI1gMAABKhsIOAJovPz+/4uJiqQNpFEWxlUfu+vfvj9lVAJoVFHYA\n0HzFxsZKtRQXF7OSBABALlDYAUDzZW5uHhcXV1BQQL8sKyvz9fXNzs5mNxUAgMxQ2AFA87V6\n9erNmzfr6upqaWm1aNHi7du3//d//8d2KAAA2aGwA4DmKyIi4vLly0KhcMeOHREREdu2bROL\nxWyHAgAV4uTkpF5XaKCwA4Dmq6CgwNXVVSAQvHjxghASGBjYrVsrjIByAAAgAElEQVS3pUuX\nshgpNzf38ePHtdsrKytFIpHy89TP3d29S5cuMmzo4uJia2sr9zwgGzc3t9zcXLZTqCgvLy8v\nLy+2UzQBCjsAaL7s7e2PHDlCP20iJSVFIBCkp6ezG2nXrl2rV6+uc5GOjo6SwzQoODhYtg2/\n/fZb+SaBjzFt2jS2I5B27dqNGzdOT0+P7SBqT20Ku/T09Nu3b9dur6ioqKmpUX4eAOCAdevW\n+fj4DB061MfHx83NTUdHp/YDXpUsKCgoKCiodrulpaWZmZny86i+jh079uvXj+0U8LHMzc0j\nIyPZTsEFalPYHT58eOPGjbXbCwsLUeADgGy8vLxyc3N1dHQCAwPt7OxycnJ8fHzYDgX1mTFj\nhqmpKbOlV69ex48fZyuPOhIIBEKhkO0UXMPj8VTkscJqU9gtW7Zs2bJltdstLS2lvuQAAI2U\nmZlpampKP2pi5MiRbMeBhqnC1U4WFhadOnViO4Xs4uLiVPC0vrrbunWrvb092ykIIUQlqksA\nACV78uSJjY1N69atzczMLl26xHYcUCe9evX6+++/2U4hO1R1ijBo0CBLS0u2UxCCwg4AmqdF\nixZ179790aNHkydP9vPz49JjxADA0dGxffv2bKdgh9qciv0wvkgkyM//72tj4//+LBaTwkJS\nU2NYXq5Lr1N7KROWYimWKn8pRRkQpfvnn3+io6NdXV2DgoJ27Njx/v37Nm3aKD8GACjCiRMn\n2I7AHkrN6evvJYRi/m/Tpv8uDQigsBRLsVT1l379dTSlXISQ7Oxs+mehUJiSkqLkAE1lYWHh\n7OysoM4HDRoUHBzMbNm1a1enTp0UtLtm69y5c/r6+mynkJ2FhcWhQ4eYLUuWLPHy8lJmhpUr\nVw4bNkxx/Ts5Of30008K6rxt27YHDhxQUOcSPErNT0C0aeMoEDgfPnxY0uLgQAz+8+//vDyS\nnEzmzJnzySefzJ49u86lTEpbeu9elqenZ2RkpJWVleqkwlIsZWXpZ5+N3Lx5yvTpk4kS8Xi8\nnJwcExMTQoixsfGTJ09U/MQNPd3JkydPFNH54MGDPTw8QkJCJC3h4eFhYWH0vM0gL+fPn/fx\n8SkpKWE7iIwsLS2/++47X19fScvSpUuTkpJOnTqltAyrVq16+PCh4q6L7dat25w5cwIDAxX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78k0j5ebm6ujoGBgYsB1EFtXV1dnZ2RYW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",
      "text/plain": [
       "Plot with title “Time Series plot of Y1”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,2))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(stepSRresidualsTS,freq=1), 48, main=\"ACF of residuals\")\n",
    "pacf(ts(stepSRresidualsTS,freq=1), 48, main=\"PACF of residuals\")\n",
    "acf(ts(stepSRresidualsTS^2,freq=1), 48, main=\"ACF of squared residuals\")\n",
    "pacf(ts(stepSRresidualsTS^2,freq=1), 48, main=\"PACF of squared residuals\")\n",
    "qqnorm(stepSRresidualsTS,main=\"Normal QQplot of residuals\")\n",
    "qqline(stepSRresidualsTS)\n",
    "plot(j1, type=\"l\", col='red', lwd=1, main=\"Time Series plot of Y1\", ylab=\"Monthly returns\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on the residual plots presented above:\n",
    "\n",
    "* From the ACF and PACF of the residuals, it seems that the assumptions with respect to them are **not** satisfied:\n",
    "  * **Restricted MA(9) (1,2,8,9 lags)** and **restricted AR(8) (1,8 lags)** must be implemented in the residuals of the stepwise regression model.\n",
    "\n",
    "* From the PACF fo the squared residuals it seems that there is an **heteroskedastisity** issue.\n",
    "  * We'll try to fix this by implementing a ARCH(8) model in the residuals\n",
    "\n",
    "* From the Normal  QQPlot of residuals several points are off the line. So we assume non normality.\n",
    "\n",
    "From the lower right plot of the return series we observe that the volatility of the return series is not constant over time. There is evidence of volatility clustering phenomenon, since there are periods of high and low volatility. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models\n",
    "\n",
    "### Restricted AR(8) for lags: 1, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in arima(stepSRresidualsTS, order = c(8, 0, 0), fixed = c(NA, 0, :\n",
      "“some AR parameters were fixed: setting transform.pars = FALSE”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = stepSRresidualsTS, order = c(8, 0, 0), fixed = c(NA, 0, 0, 0, 0, 0, \n",
       "    0, NA, NA))\n",
       "\n",
       "Coefficients:\n",
       "         ar1  ar2  ar3  ar4  ar5  ar6  ar7     ar8  intercept\n",
       "      0.1921    0    0    0    0    0    0  0.2093     -1e-04\n",
       "s.e.  0.0741    0    0    0    0    0    0  0.0758      8e-04\n",
       "\n",
       "sigma^2 estimated as 4.266e-05:  log likelihood = 606.64,  aic = -1205.27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar8restricted <- arima(stepSRresidualsTS, \n",
    "                       order=c(8,0,0), \n",
    "                       fixed= c(NA, 0,0,0,0,0,0,NA, NA))\n",
    "ar8restricted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y1r9fPzu3r1alhYWL9+/fDsRADQvpeNNsH5syEuXrx49OjR+u2FhYUVFRXLli1T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sws7BweHmzZtyHBgbVFd6enqvXr2Sk5M5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\nAKY+wIAhwyfbAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Normal QQplot on residuals of restricted AR”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,2))\n",
    "ar8restricted_residualsTS <- ts(ar8restricted$residuals, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(ar8restricted_residualsTS,freq=1), 48, main=\"ACF on residuals of restricted AR\")\n",
    "pacf(ts(ar8restricted_residualsTS,freq=1), 48, main=\"PACF on residuals of restricted AR\")\n",
    "acf(ts(ar8restricted_residualsTS^2,freq=1), 48, main=\"ACF on squared residuals of restricted AR\")\n",
    "pacf(ts(ar8restricted_residualsTS^2,freq=1), 48, main=\"PACF on squared residuals of restricted AR\")\n",
    "qqnorm(ar8restricted_residualsTS,main=\"Normal QQplot on residuals of restricted AR\")\n",
    "qqline(ar8restricted_residualsTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  ar8restricted_residualsTS\n",
       "X-squared = 11.981, df = 12, p-value = 0.4472\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  ar8restricted_residualsTS^2\n",
       "X-squared = 13.788, df = 12, p-value = 0.3145\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Box.test(ar8restricted_residualsTS, lag=12, type=\"Ljung\")\n",
    "Box.test(ar8restricted_residualsTS^2, lag=12, type=\"Ljung\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restricted MA(9) (1, 2, 8, 9 lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = stepSRresidualsTS, order = c(0, 0, 9), fixed = c(NA, NA, 0, 0, 0, \n",
       "    0, 0, NA, NA, NA))\n",
       "\n",
       "Coefficients:\n",
       "         ma1     ma2  ma3  ma4  ma5  ma6  ma7     ma8     ma9  intercept\n",
       "      0.0985  0.1208    0    0    0    0    0  0.1994  0.2048     -1e-04\n",
       "s.e.  0.0773  0.0777    0    0    0    0    0  0.0735  0.0759      8e-04\n",
       "\n",
       "sigma^2 estimated as 4.144e-05:  log likelihood = 608.92,  aic = -1205.84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma9restricted <- arima(stepSRresidualsTS, \n",
    "                       order=c(0,0,9), \n",
    "                       fixed= c(NA, NA, 0, 0, 0, 0, 0, NA, NA, NA))\n",
    "ma9restricted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqqqqoqrqNoFfr163fmzBnFfV6+fLlo0SL1xAMthPyh2MTExJCQELnGKVOm7Nq1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",
      "text/plain": [
       "Plot with title “Normal QQplot on residuals of restricted MA”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,2))\n",
    "ma9restricted_residualsTS <- ts(ma9restricted$residuals, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(ma9restricted_residualsTS,freq=1), 48, main=\"ACF on residuals of restricted MA\")\n",
    "pacf(ts(ma9restricted_residualsTS,freq=1), 48, main=\"PACF on residuals of restricted MA\")\n",
    "acf(ts(ma9restricted_residualsTS^2,freq=1), 48, main=\"ACF on squared residuals of restricted MA\")\n",
    "pacf(ts(ma9restricted_residualsTS^2,freq=1), 48, main=\"PACF on squared residuals of restricted MA\")\n",
    "qqnorm(ma9restricted_residualsTS,main=\"Normal QQplot on residuals of restricted MA\")\n",
    "qqline(ma9restricted_residualsTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  ma9restricted_residualsTS\n",
       "X-squared = 7.5372, df = 12, p-value = 0.8202\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  ma9restricted_residualsTS^2\n",
       "X-squared = 11.308, df = 12, p-value = 0.5027\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Box.test(ma9restricted_residualsTS, lag=12, type=\"Ljung\")\n",
    "Box.test(ma9restricted_residualsTS^2, lag=12, type=\"Ljung\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restricted ARMA(8, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in arima(stepSRresidualsTS, order = c(8, 0, 9), fixed = c(NA, 0, :\n",
      "“some AR parameters were fixed: setting transform.pars = FALSE”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = stepSRresidualsTS, order = c(8, 0, 9), fixed = c(NA, 0, 0, 0, 0, 0, \n",
       "    0, NA, NA, NA, 0, 0, 0, 0, 0, NA, NA, NA))\n",
       "\n",
       "Coefficients:\n",
       "         ar1  ar2  ar3  ar4  ar5  ar6  ar7      ar8      ma1     ma2  ma3  ma4\n",
       "      0.4686    0    0    0    0    0    0  -0.0349  -0.3497  0.1246    0    0\n",
       "s.e.  0.2094    0    0    0    0    0    0   0.2070   0.2100  0.0800    0    0\n",
       "      ma5  ma6  ma7     ma8     ma9  intercept\n",
       "        0    0    0  0.2246  0.1166     -1e-04\n",
       "s.e.    0    0    0  0.1894  0.1013      1e-03\n",
       "\n",
       "sigma^2 estimated as 4.065e-05:  log likelihood = 610.52,  aic = -1205.05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma89restricted <- arima(stepSRresidualsTS, \n",
    "                          \n",
    "                       order=c(8,0,9), \n",
    "                       fixed= c(NA, 0, 0, 0, 0, 0, 0, NA, NA, NA, 0, 0,0,0, 0, NA, NA, NA))\n",
    "arma89restricted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wAAcEgikUgkEq6jAGi7pG93EhMT4+Pjk5ycTP/Zp08fXV3dQYMGyWsy3YsXL27f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\nCCunygAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Normal QQplot on residuals of restricted MA”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(3,2))\n",
    "arma89restricted_residualsTS <- ts(arma89restricted$residuals, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(arma89restricted_residualsTS,freq=1), 48, main=\"ACF on residuals of restricted ARMA\")\n",
    "pacf(ts(arma89restricted_residualsTS,freq=1), 48, main=\"PACF on residuals of restricted ARMA\")\n",
    "acf(ts(arma89restricted_residualsTS^2,freq=1), 48, main=\"ACF on squared residuals of restricted ARMA\")\n",
    "pacf(ts(arma89restricted_residualsTS^2,freq=1), 48, main=\"PACF on squared residuals of restricted ARMA\")\n",
    "qqnorm(arma89restricted_residualsTS,main=\"Normal QQplot on residuals of restricted MA\")\n",
    "qqline(arma89restricted_residualsTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  arma89restricted_residualsTS\n",
       "X-squared = 3.0913, df = 12, p-value = 0.9949\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBox-Ljung test\n",
       "\n",
       "data:  arma89restricted_residualsTS^2\n",
       "X-squared = 13.614, df = 12, p-value = 0.3261\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Box.test(arma89restricted_residualsTS, lag=12, type=\"Ljung\")\n",
    "Box.test(arma89restricted_residualsTS^2, lag=12, type=\"Ljung\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on the residual plots and the Box Tests of the 3 different models presented above:\n",
    "\n",
    "* From the ACF and PACF of the residuals:\n",
    "\n",
    "  * **Restricted AR**: It seems that the assumptions with respect to them are satisfied\n",
    "  * **Restricted MA**: It seems that the assumptions with respect to them are satisfied\n",
    "  * **Restricted ARMA**: It seems that the assumptions with respect to them are satisfied\n",
    "\n",
    "\n",
    "* From the PACF fo the **squared** residuals:\n",
    "  * **Restricted AR**: It seems that the assumptions regarding homoskedasticity are satisfied\n",
    "  * **Restricted MA**: It seems that the assumptions regarding homoskedasticity are satisfied\n",
    "  * **Restricted ARMA**: It seems that the assumptions regarding homoskedasticity are satisfied\n",
    "\n",
    "\n",
    "* From the Normal QQPlot of residuals **for all 3 models** normality is **rejected**!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarizing we have the following:\n",
    "\n",
    "* Restricted AR(8): \n",
    "  * Variance: **4.266e-05**\n",
    "  * AIC: **-1205.27**\n",
    "* Restricted MA(9):\n",
    "  * Variance: **4.144e-05**\n",
    "  * AIC: **-1205.84**\n",
    "* Restricted ARMA(8, 9)\n",
    "  * Variance: **4.065e-05**\n",
    "  * AIC: **-1205.05**\n",
    "\n",
    "  \n",
    "### Conclusion:\n",
    "\n",
    "* The model with the lowest variance and lower AIC score is the best. \n",
    "* Here, **restricted ARMA(8, 9)** has both the smallest variance and smallest AIC. Thus, it's the model that best fits the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Heteroskedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Title:\n",
      " GARCH Modelling \n",
      "\n",
      "Call:\n",
      " garchFit(formula = ~garch(1, 0), data = stepSR$residuals, trace = F) \n",
      "\n",
      "Mean and Variance Equation:\n",
      " data ~ garch(1, 0)\n",
      "<environment: 0x6047988>\n",
      " [data = stepSR$residuals]\n",
      "\n",
      "Conditional Distribution:\n",
      " norm \n",
      "\n",
      "Coefficient(s):\n",
      "         mu        omega       alpha1  \n",
      "-8.3325e-19   3.9679e-05   1.5778e-01  \n",
      "\n",
      "Std. Errors:\n",
      " based on Hessian \n",
      "\n",
      "Error Analysis:\n",
      "         Estimate  Std. Error  t value Pr(>|t|)    \n",
      "mu     -8.333e-19   5.454e-04    0.000    1.000    \n",
      "omega   3.968e-05   6.400e-06    6.200 5.63e-10 ***\n",
      "alpha1  1.578e-01   1.240e-01    1.272    0.203    \n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Log Likelihood:\n",
      " 599.9101    normalized:  3.570894 \n",
      "\n",
      "Description:\n",
      " Mon Mar  5 12:52:00 2018 by user:  \n",
      "\n",
      "\n",
      "Standardised Residuals Tests:\n",
      "                                Statistic p-Value     \n",
      " Jarque-Bera Test   R    Chi^2  4.584752  0.1010261   \n",
      " Shapiro-Wilk Test  R    W      0.9840617 0.05148097  \n",
      " Ljung-Box Test     R    Q(10)  39.71048  1.905359e-05\n",
      " Ljung-Box Test     R    Q(15)  41.96158  0.0002278329\n",
      " Ljung-Box Test     R    Q(20)  43.71244  0.001644186 \n",
      " Ljung-Box Test     R^2  Q(10)  11.60545  0.3123296   \n",
      " Ljung-Box Test     R^2  Q(15)  16.40743  0.3555027   \n",
      " Ljung-Box Test     R^2  Q(20)  20.02109  0.4566114   \n",
      " LM Arch Test       R    TR^2   12.10482  0.437299    \n",
      "\n",
      "Information Criterion Statistics:\n",
      "      AIC       BIC       SIC      HQIC \n",
      "-7.106073 -7.050288 -7.106696 -7.083433 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# First, we will fit an ARCH(1) model:\n",
    "m1arch <- garchFit(~garch(1,0), data=stepSR$residuals, trace=F)\n",
    "# trace = F reduces the summary\n",
    "summary(m1arch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain the parameter estimates,\n",
    "the corresponding standard errors, the t-statistics and the associated p-values, in order to examine the\n",
    "statistical significance of the model parameters. \n",
    "\n",
    "The ARCH(1) model is written:\n",
    "\n",
    "$$y_{t} = μ + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t} \\thicksim N(0,1), $$\n",
    "$$σ^2_{t} = a_{0} + a_{1}*e^2_{t-1} $$\n",
    "\n",
    "Thus, the estimated ARCH(1) model is:\n",
    "$$y_{t} = -1.1216e-18 + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t} \\thicksim N(0,1), $$\n",
    "$$σ^2_{t} = 0.000039679 + 0.15778*e^2_{t-1} $$\n",
    "\n",
    "\n",
    "There are different diagnostic tests outlined in the aformentioned summary, performed on the residuals $R$ and/or the squared residuals $R^2$. \n",
    "\n",
    "The **Jarque-Bera** and the **Shapiro-Wilk** tests are used in order to examine the normality assumption of the model residuals. It seems that the null hypothesis of normality (p values 0.1010261 and 0.05148097) cannot be rejected, i.e. there is evidence of **normality** of residuals. \n",
    "\n",
    "Most of the remaining tests are based on the Ljung-Box Q-statistic to examine the autocorrelation of the residuals and/or the squared residuals. The results indicate that there **exists autocorrelation** in the **residuals** series. On the contrary the tests indicate **no autocorrelation in the squared residuals**.\n",
    "\n",
    "That is, the ARCH(1) model did not capture adequately the time-varying characteristics of the return series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>meanForecast</th><th scope=col>meanError</th><th scope=col>standardDeviation</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006381814</td><td>0.006381814</td></tr>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006790083</td><td>0.006790083</td></tr>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006852279</td><td>0.006852279</td></tr>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006862041</td><td>0.006862041</td></tr>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006863580</td><td>0.006863580</td></tr>\n",
       "\t<tr><td>-8.3325e-19</td><td>0.006863823</td><td>0.006863823</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " meanForecast & meanError & standardDeviation\\\\\n",
       "\\hline\n",
       "\t -8.3325e-19 & 0.006381814 & 0.006381814\\\\\n",
       "\t -8.3325e-19 & 0.006790083 & 0.006790083\\\\\n",
       "\t -8.3325e-19 & 0.006852279 & 0.006852279\\\\\n",
       "\t -8.3325e-19 & 0.006862041 & 0.006862041\\\\\n",
       "\t -8.3325e-19 & 0.006863580 & 0.006863580\\\\\n",
       "\t -8.3325e-19 & 0.006863823 & 0.006863823\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "meanForecast | meanError | standardDeviation | \n",
       "|---|---|---|---|---|---|\n",
       "| -8.3325e-19 | 0.006381814 | 0.006381814 | \n",
       "| -8.3325e-19 | 0.006790083 | 0.006790083 | \n",
       "| -8.3325e-19 | 0.006852279 | 0.006852279 | \n",
       "| -8.3325e-19 | 0.006862041 | 0.006862041 | \n",
       "| -8.3325e-19 | 0.006863580 | 0.006863580 | \n",
       "| -8.3325e-19 | 0.006863823 | 0.006863823 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  meanForecast meanError   standardDeviation\n",
       "1 -8.3325e-19  0.006381814 0.006381814      \n",
       "2 -8.3325e-19  0.006790083 0.006790083      \n",
       "3 -8.3325e-19  0.006852279 0.006852279      \n",
       "4 -8.3325e-19  0.006862041 0.006862041      \n",
       "5 -8.3325e-19  0.006863580 0.006863580      \n",
       "6 -8.3325e-19  0.006863823 0.006863823      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(m1arch, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Student t ARCH model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Title:\n",
      " GARCH Modelling \n",
      "\n",
      "Call:\n",
      " garchFit(formula = ~garch(1, 0), data = stepSR$residuals, cond.dist = \"std\", \n",
      "    trace = F) \n",
      "\n",
      "Mean and Variance Equation:\n",
      " data ~ garch(1, 0)\n",
      "<environment: 0x3a8a8b0>\n",
      " [data = stepSR$residuals]\n",
      "\n",
      "Conditional Distribution:\n",
      " std \n",
      "\n",
      "Coefficient(s):\n",
      "         mu        omega       alpha1        shape  \n",
      "-8.3325e-19   4.0900e-05   2.1345e-01   1.0000e+01  \n",
      "\n",
      "Std. Errors:\n",
      " based on Hessian \n",
      "\n",
      "Error Analysis:\n",
      "         Estimate  Std. Error  t value Pr(>|t|)    \n",
      "mu     -8.333e-19   5.450e-04    0.000   1.0000    \n",
      "omega   4.090e-05   7.825e-06    5.227 1.73e-07 ***\n",
      "alpha1  2.134e-01   1.600e-01    1.334   0.1821    \n",
      "shape   1.000e+01   4.108e+00    2.434   0.0149 *  \n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Log Likelihood:\n",
      " 598.2538    normalized:  3.561035 \n",
      "\n",
      "Description:\n",
      " Mon Mar  5 12:52:01 2018 by user:  \n",
      "\n",
      "\n",
      "Standardised Residuals Tests:\n",
      "                                Statistic p-Value     \n",
      " Jarque-Bera Test   R    Chi^2  4.888336  0.08679832  \n",
      " Shapiro-Wilk Test  R    W      0.983019  0.03793478  \n",
      " Ljung-Box Test     R    Q(10)  38.5695   3.019439e-05\n",
      " Ljung-Box Test     R    Q(15)  40.78325  0.0003450467\n",
      " Ljung-Box Test     R    Q(20)  42.58153  0.002321082 \n",
      " Ljung-Box Test     R^2  Q(10)  12.06843  0.2805031   \n",
      " Ljung-Box Test     R^2  Q(15)  16.70214  0.3369802   \n",
      " Ljung-Box Test     R^2  Q(20)  20.35781  0.4357565   \n",
      " LM Arch Test       R    TR^2   12.3613   0.4171166   \n",
      "\n",
      "Information Criterion Statistics:\n",
      "      AIC       BIC       SIC      HQIC \n",
      "-7.074450 -7.000070 -7.075549 -7.044263 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "m1arch_student=garchFit(~garch(1,0),data=stepSR$residuals, cond.dist=\"std\",trace=F)\n",
    "summary(m1arch_student)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ARCH(1) model based on Student-t errors is:\n",
    "\n",
    "$$y_{t} = μ + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t},  v_{t}\\thicksim Student-t_{1.0000e+01}, $$\n",
    "$$σ^2_{t} = a_{0} + a_{1}*e^2_{t-1} $$\n",
    "\n",
    "Thus, the estimated ARCH(1) model is:\n",
    "$$y_{t} = -8.3325e-19 + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t},  v_{t}\\thicksim Student-t_{1.0000e+01}, $$\n",
    "$$σ^2_{t} = 0.0000409 + 0.21345*e^2_{t-1} $$\n",
    "\n",
    "\n",
    "There are different diagnostic tests outlined in the aformentioned summary, performed on the residuals $R$ and/or the squared residuals $R^2$. \n",
    "\n",
    "The **Jarque-Bera** and the **Shapiro-Wilk** tests are used in order to examine the normality assumption of the model residuals. It seems that the null hypothesis of normality (p values 0.08679832 and 0.03793478) cannot surely be rejected, i.e. there is *some* evidence of **normality** of residuals. \n",
    "\n",
    "Most of the remaining tests are based on the Ljung-Box Q-statistic to examine the autocorrelation of the residuals and/or the squared residuals. The results indicate that there **exists autocorrelation** in the **residuals** series. On the contrary the tests indicate some autocorrelation in the squared residuals.\n",
    "\n",
    "That is, the ARCH(1) model did not capture adequately the time-varying characteristics of the return series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GARCH Models\n",
    "\n",
    "We can estimate a GARCH(1,1) model based on normal distribution for the error process. The following\n",
    "set of commands estimates the model, presents a summary of the obtained results, gives several graphs\n",
    "to evaluate the model fit, and computes predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Title:\n",
      " GARCH Modelling \n",
      "\n",
      "Call:\n",
      " garchFit(formula = ~garch(1, 1), data = stepSR$residuals, trace = F) \n",
      "\n",
      "Mean and Variance Equation:\n",
      " data ~ garch(1, 1)\n",
      "<environment: 0x1fdb868>\n",
      " [data = stepSR$residuals]\n",
      "\n",
      "Conditional Distribution:\n",
      " norm \n",
      "\n",
      "Coefficient(s):\n",
      "         mu        omega       alpha1        beta1  \n",
      "-8.3325e-19   3.9679e-05   1.5778e-01   1.0000e-08  \n",
      "\n",
      "Std. Errors:\n",
      " based on Hessian \n",
      "\n",
      "Error Analysis:\n",
      "         Estimate  Std. Error  t value Pr(>|t|)\n",
      "mu     -8.333e-19   5.584e-04    0.000    1.000\n",
      "omega   3.968e-05   3.177e-05    1.249    0.212\n",
      "alpha1  1.578e-01   1.704e-01    0.926    0.355\n",
      "beta1   1.000e-08   7.692e-01    0.000    1.000\n",
      "\n",
      "Log Likelihood:\n",
      " 599.9101    normalized:  3.570894 \n",
      "\n",
      "Description:\n",
      " Mon Mar  5 12:52:01 2018 by user:  \n",
      "\n",
      "\n",
      "Standardised Residuals Tests:\n",
      "                                Statistic p-Value     \n",
      " Jarque-Bera Test   R    Chi^2  4.584748  0.1010263   \n",
      " Shapiro-Wilk Test  R    W      0.9840617 0.05148117  \n",
      " Ljung-Box Test     R    Q(10)  39.71049  1.905348e-05\n",
      " Ljung-Box Test     R    Q(15)  41.96159  0.0002278317\n",
      " Ljung-Box Test     R    Q(20)  43.71246  0.001644178 \n",
      " Ljung-Box Test     R^2  Q(10)  11.60544  0.31233     \n",
      " Ljung-Box Test     R^2  Q(15)  16.40742  0.3555029   \n",
      " Ljung-Box Test     R^2  Q(20)  20.02108  0.4566116   \n",
      " LM Arch Test       R    TR^2   12.10481  0.4372992   \n",
      "\n",
      "Information Criterion Statistics:\n",
      "      AIC       BIC       SIC      HQIC \n",
      "-7.094168 -7.019788 -7.095267 -7.063981 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "m2garch=garchFit(~garch(1,1),data=stepSR$residuals,trace=F)\n",
    "summary(m2garch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The GARCH(1, 1) model is written:\n",
    "\n",
    "$$y_{t} = μ + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t},  v_{t}\\thicksim N(0,1), $$\n",
    "$$σ^2_{t} = a_{0} + a_{1}*e^2_{t-1} +  b_{1}*σ^2_{t-1} $$\n",
    "\n",
    "Thus, the estimated GARCH(1, 1) model is:\n",
    "$$y_{t} = -8.3325* 10^{-19} + e_{t} , $$\n",
    "$$e_{t} = σ_{t}*v_{t} \\thicksim N(0,1), $$\n",
    "$$σ^2_{t} = 3.9679 * 10^{-5} + 1.5778*10^{-1}e^2_{t-1} +  10^{-8}*σ^2_{t-1} $$\n",
    "\n",
    "\n",
    "There are different diagnostic tests outlined in the aformentioned summary, performed on the residuals $R$ and/or the squared residuals $R^2$. \n",
    "\n",
    "The **Jarque-Bera** and the **Shapiro-Wilk** tests are used in order to examine the normality assumption of the model residuals. It seems that the null hypothesis of normality (p values 0.1010263 and 0.05148117) cannot be rejected, i.e. there is evidence of **normality** of residuals. \n",
    "\n",
    "Most of the remaining tests are based on the Ljung-Box Q-statistic to examine the autocorrelation of the residuals and/or the squared residuals. \n",
    "\n",
    "The results indicate that there **exists autocorrelation** in the **residuals** series. On the contrary the tests indicate **no autocorrelation in the squared residuals**.\n",
    "\n",
    "That is, the GARCH(1, 1) model did not capture adequately the time-varying characteristics of the return series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined ARMA + GARCH Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Title:\n",
      " GARCH Modelling \n",
      "\n",
      "Call:\n",
      " garchFit(formula = ~arma(1, 1) + garch(1, 1), data = stepSR$residuals, \n",
      "    trace = F) \n",
      "\n",
      "Mean and Variance Equation:\n",
      " data ~ arma(1, 1) + garch(1, 1)\n",
      "<environment: 0x58de7a0>\n",
      " [data = stepSR$residuals]\n",
      "\n",
      "Conditional Distribution:\n",
      " norm \n",
      "\n",
      "Coefficient(s):\n",
      "         mu          ar1          ma1        omega       alpha1        beta1  \n",
      "-8.3325e-19   8.2028e-01  -6.4688e-01   3.2707e-06   6.6956e-03   9.1483e-01  \n",
      "\n",
      "Std. Errors:\n",
      " based on Hessian \n",
      "\n",
      "Error Analysis:\n",
      "         Estimate  Std. Error  t value Pr(>|t|)    \n",
      "mu     -8.333e-19   1.819e-04    0.000  1.00000    \n",
      "ar1     8.203e-01   1.533e-01    5.350 8.78e-08 ***\n",
      "ma1    -6.469e-01   1.989e-01   -3.253  0.00114 ** \n",
      "omega   3.271e-06   5.953e-06    0.549  0.58269    \n",
      "alpha1  6.696e-03   3.203e-02    0.209  0.83443    \n",
      "beta1   9.148e-01   1.464e-01    6.247 4.18e-10 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Log Likelihood:\n",
      " 605.9441    normalized:  3.60681 \n",
      "\n",
      "Description:\n",
      " Mon Mar  5 12:52:01 2018 by user:  \n",
      "\n",
      "\n",
      "Standardised Residuals Tests:\n",
      "                                Statistic p-Value   \n",
      " Jarque-Bera Test   R    Chi^2  4.777204  0.09175789\n",
      " Shapiro-Wilk Test  R    W      0.9826981 0.03454298\n",
      " Ljung-Box Test     R    Q(10)  10.62184  0.3877289 \n",
      " Ljung-Box Test     R    Q(15)  12.14243  0.6682181 \n",
      " Ljung-Box Test     R    Q(20)  14.76602  0.7896358 \n",
      " Ljung-Box Test     R^2  Q(10)  12.97631  0.2249994 \n",
      " Ljung-Box Test     R^2  Q(15)  16.81431  0.3300855 \n",
      " Ljung-Box Test     R^2  Q(20)  20.39687  0.4333625 \n",
      " LM Arch Test       R    TR^2   15.90855  0.1954609 \n",
      "\n",
      "Information Criterion Statistics:\n",
      "      AIC       BIC       SIC      HQIC \n",
      "-7.142192 -7.030622 -7.144628 -7.096911 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "arma11_garch11fit <- garchFit(formula= ~arma(1,1) + garch(1,1),\n",
    "                              data=stepSR$residuals,\n",
    "                              trace=F)\n",
    "summary(arma11_garch11fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different diagnostic tests outlined in the aformentioned summary, performed on the residuals $R$ and/or the squared residuals $R^2$. \n",
    "\n",
    "The **Jarque-Bera** and the **Shapiro-Wilk** tests are used in order to examine the normality assumption of the model residuals. It seems that the null hypothesis of normality (p values 0.09175789 and 0.03454298) is rejected, i.e. there is evidence of **non normality** of residuals. \n",
    "\n",
    "Most of the remaining tests are based on the Ljung-Box Q-statistic to examine the autocorrelation of the residuals and/or the squared residuals. \n",
    "\n",
    "The results indicate that there is **no autocorrelation** in the **residuals** series. Also, the tests indicate **no autocorrelation in the squared residuals**.\n",
    "\n",
    "That is, **ARMA(1,1) + GARCH(1,1)** model **did capture** adequately the time-varying characteristics of the return series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HYA6AuEnQ7AsQMA+AGOHQDaA2GnA7RH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\nAABAEQQ7AAAARfwfLynH7wdLs1MAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast <- predict(arma11_garch11fit, 12)\n",
    "\n",
    "# plot of forecasts with 1 standard error\n",
    "par(mfrow=c(1,1))\n",
    "UL <- forecast$meanForecast + forecast$meanError\n",
    "LL <- forecast$meanForecast - forecast$meanError\n",
    "\n",
    "predTS <- ts(forecast$meanForecast, frequency=12, start = c(2004, 12))\n",
    "UL_TS <- ts(forecast$meanForecast, frequency=12, start = c(2004, 12))\n",
    "LL_TS <- ts(forecast$meanForecast, frequency=12, start = c(2004, 12))\n",
    "\n",
    "min_x <- min(stepSRresidualsTS, LL)\n",
    "max_x <- max(stepSRresidualsTS, UL)\n",
    "\n",
    "ts.plot(stepSRresidualsTS, predTS)\n",
    "lines(predTS, col=\"red\", type=\"o\")\n",
    "lines(UL_TS, col=\"blue\", lty=\"dashed\")\n",
    "lines(LL_TS, col=\"blue\", lty=\"dashed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5.02855687224207e-05"
      ],
      "text/latex": [
       "5.02855687224207e-05"
      ],
      "text/markdown": [
       "5.02855687224207e-05"
      ],
      "text/plain": [
       "[1] 5.028557e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(hydroGOF)\n",
    "MSE <- mse(forecast$meanForecast, forecast$meanError)\n",
    "MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$MSE = 5.028* 10^{-5}$$"
   ]
  }
 ],
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